Paper Nums:100+ |
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Multivariable |
Electricity PEMSD7M BikeNYC TimesNet_data |
SCNN |
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/e8d0bd162dab9811adb0c07c30fb7cd00324b930d8d6c8c5edf52c699da1c0af/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4a4c44656e672f53434e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
TKDE 2024 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
DeepTime (Framework, Fourier Features, Meta-optimization) |
Learning Deep Time-index Models for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/3f7e12cef9c3e7c906ebb2f4168b964a93023750054be3c15ffc3c86b45a19b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f4465657054696d653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2023 |
Multivariable |
Synthetic Taxi Electricity Traffic |
FeatureP (Feature Enhancement) |
Feature Programming for Multivariate Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/c6716cab76e59095005da6321828dd048ac8462bbcb5a11d51da9d838c1ad52c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f536972416c65783930302f4665617475726550726f6772616d6d696e673f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2023 |
Multivariable |
NorPool Caiso Weather ETT Wind Traffic Electricity Exchange |
TimeDiff |
Non-autoregressive Conditional Diffusion Models for Time Series Prediction |
None |
ICML 2023 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
MICN |
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/8a572da3adfe56608ce67fe9528520ddc50d6dcd8c216b7b8500b3a50329a1dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77616e67687132312f4d49434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
ETT Weather Electricity ILI Traffic |
Crossformer |
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/c5163b352fd3c8a0f7f38c3e7969ef284f38f6c7df07aae615b9b2bccf4ba56b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5468696e6b6c61622d534a54552f43726f7373666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Forecast Imputation Classifi AnomalyDet |
ETT M4 Electricity Weather SMD,MSL SMAP,SWaT PSM |
TimesNet |
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis |
Pytorch
![Forks](https://camo.githubusercontent.com/50ae9151ae8e3cfbb4176f4ec81d863184e685da0585d860f23516167394ef84/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7468756d6c2f54696d652d5365726965732d4c6962726172793f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
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Meta-SSM |
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/82ccb87001cf521c4f270ae0a77200fecaeee48f3aeef1a58d5f15dee8232ca1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a6f686e2d782d6a69616e672f6d6574615f73736d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
ETT Electricity Traffic Weather |
FSNet |
Learning Fast and Slow for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/4de68e91ea8f7e1d1fb576d9c5f59f0a78df5e35ff24c46e4b9eaa777cece1a4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f66736e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Robust Multivariable |
Traffic Taxi Wiki Electricity |
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Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms |
Amazon |
ICLR 2023 |
Multivariable |
Electricity Crypto M4 Traffic Exchange |
KNF |
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts |
Pytorch
![Forks](https://camo.githubusercontent.com/b25f60754b9c6ba6021e77fb2ffc5cc10b6eb36afb0c4f79717c68c43d5a40e3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f676f6f676c652d72657365617263682f676f6f676c652d72657365617263682f747265652f6d61737465722f4b4e463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
ETT Weather Electricity Traffic Exchange |
SpaceTime |
Effectively Modeling Time Series with Simple Discrete State Spaces |
Pytorch
![Forks](https://camo.githubusercontent.com/0a4ad631210c4b8b7e8cbcaf3396cfd5b8cc7fa4f4d6ed017a4359aab54f71b2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f48617a7952657365617263682f737061636574696d653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
Weather Traffic Electricity ILI ETT |
PatchTST |
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers |
Pytorch
![Forks](https://camo.githubusercontent.com/f9e757403c6aca963d0b70a6e2d32bd3d3c175e05b200ca8f4dfd7c1ff440144/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f797571696e696539382f50617463685453543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
Exchange Weather Electricity Traffic ILI |
Scaleformer |
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/19163ea24be88290bad494bfbbee65c4071f826dd888a9ffb4bdd45dea6dc651/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f426f7265616c697341492f7363616c65666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable classification AnomalyDec |
Electricity Weather ETTm1 MSL SMD SMAP |
SBT |
Sparse Binary Transformers for Multivariate Time Series Modeling |
Pytorch
![Forks](https://camo.githubusercontent.com/ab2a948c537502ed11120815a8fcab86122e315d654e96908add4322a063fc2d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d617474676f72622f7370617273652d62696e6172792d7472616e73666f726d6572733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Multivariable |
SIP METR-LA KnowAir Electricity |
CauSTG |
Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning |
Pytorch
![Forks](https://camo.githubusercontent.com/3db8472556410c5ab1a96d64c1c14ffa5681aa6ddf37469838bd84271cdc1273/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a7a7979303932392f4b444432332d4361755354473f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Robust Multivariable |
PEMS-BAY PEMS04 |
RDAT |
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training |
Pytorch
![Forks](https://camo.githubusercontent.com/3d5e94b26fde5b0c6a320612aa9000129e8c313ef581687eb16370b402696706/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f757361696c2d686b7573742f524441543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Multivariable |
Beijing Chengdu Harbin |
Frigate |
Frigate: Frugal Spatio-temporal Forecasting on Road Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/5b570badf7da71dd990bb989d101cc5237a2fc999ccb6ffe044dac283acc7925/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f696465612d696974642f667269676174653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Multivariable |
XC-Traffic NYC-Traffic |
GCIM |
Generative Causal Interpretation Model for Spatio-Temporal Representation Learning |
None |
KDD 2023 |
Multivariable |
Tourism Labour Wiki Flu-Symptoms FB-Survey |
PROFHiT |
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b80aaf30a3a77543fa7fbaa6513a1afdd9bd542c5682273bb34ef855a15f0ef5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4164697479614c61622f50726f666869743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Multivariable Under Miss |
AQI-36 AQI PEMS-BAY CER-E Healthcare SMAP |
MIDM |
An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series |
Author |
KDD 2023 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 etc. |
Localised |
Localised Adaptive Spatial-Temporal Graph Neural Network |
None |
KDD 2023 |
Multivariable |
PEMS3-Stream |
PECPM |
Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction |
None |
KDD 2023 |
Multivariable |
Tourism Wiki Traffic |
HPO |
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting |
None |
KDD 2023 |
Multivariable |
Weather Traffic Electricity ETT |
TSMixer |
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting |
None |
KDD 2023 |
Transfer Traffic Forecasting |
PEMSD7M PEMSD7M METR-LA PEMS-BAY |
TransGTR |
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities |
Author |
KDD 2023 |
Multivariable |
ETT Traffic Electricity Exchange Weather ILI |
DLinear |
Are Transformers Effective for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/07e509e541de6eb5a41bbfa6d6d835a04c1fb5a15918e199e40286cb99a20747/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f637572652d6c61622f4c5453462d4c696e6561723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
METR-LA PEMSD7M |
STC-Dropout |
Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout |
Pytorch
![Forks](https://camo.githubusercontent.com/018f9b81cec9ddc672b4e777d4078ca516dae93dd30beebac6e925bf2cb74977/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f557262616e2d436f6d707574696e672f5354432d44726f706f75743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
BJ-Bike NYC-Bike |
STNSCM |
Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/6b85004aee70487822453ef0e31771a3ce9ddb17f40ad3c6f309b7d0df416f7d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f457465726e6974795a592f53544e53434d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
XC-Trans XC-Speed |
CCHMM |
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/73f41280681784b7ff244f55779d624dba1d5b886fd3c77f09aa07102b36829a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f457465726e6974795a592f4343484d4d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
NYCBike1 NYCBike2 NYCTaxi BJTaxi |
ST-SSL |
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/edf0165c930abfc08d760614326ea5a28259fb932138453f5c362c5c0273f1df/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4563686f2d4a692f53542d53534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
PV-US CER-En |
SGP |
Scalable Spatiotemporal Graph Neural Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/0bf0eddeca4c9fbe30dd6b1b388919f24f9c4cf9f51034623d7de580f3a8790e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f47726170682d4d616368696e652d4c6561726e696e672d47726f75702f7367703f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
Electricity Solar PEMS-BAY METR-LA |
SRD |
Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling |
Pytorch
![Forks](https://camo.githubusercontent.com/c346a7f09b2d590d8122c525e414f56b22802a12139921b1777c5631388baf77/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4172746875722d4e756c6c2f5352443f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
ETT Electricity |
InfoTS |
Time Series Contrastive Learning with Information-Aware Augmentations |
Pytorch
![Forks](https://camo.githubusercontent.com/a967c53d8f128b45097a3487e100843e589185a9f986e28883b7b31df25a3940/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6368656e677730372f496e666f54533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
PhysioNet MIMIC-III Activity Appliances Energy |
PrimeNet |
PrimeNet: Pre-training for Irregular Multivariate Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/e6a21222775f78cfca266584d6928856d4f1d2aad84a365f36826c321cb2c509/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f72616e616b726f7963686f7764687572792f5072696d654e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
Electricity ETT Weather |
Dish-TS |
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b276453e6f54535aaad2dcea73f0cf268e50eb3b84f709170147f1da18c5f265/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77656966616e74742f446973682d54533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
NHITS |
NHITS: Neural Hierarchical Interpolation for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/2b357e82149ba7d23bc9e9240db57ebcb07e95d8d0f2a21cf67a5bdf96dab1cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4e6978746c612f6e657572616c666f7265636173743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
METR-LA ETT Weather |
MegaCRN |
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/df9a4de5ea485a268dfc1d7e3b6bd5c20bf394e579a9540f929869ab0bff686d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f4d65676143524e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
Santa Traffic |
NEC+ |
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/435b75141200313692e420022de885c383a959ea8de0b3d4cc6ea2032b7f78d5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6461766964616e617374617369752f4e4543506c75733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Extreme MTSF |
Electricity Solar Weather Traffic |
WaveForM |
WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/c10c1882e45e418a289abd03e1b494d65973a038301f2e96976681ba96423faf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c616e796f756e67434e2f57617665466f724d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
PEMS04 PEMS07 PEMS08 NYCTaxi CHBike TDrive |
PDFormer |
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/8322b28760c616276807ecd866c2dd83e47c8cd7e5ede3ea819b13b2d7a81981/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4255414142494753436974792f5044466f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
AmapBeijing AmapChengdu |
STGNPP |
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction |
None |
AAAI 2023 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
InParformer |
InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting |
None |
AAAI 2023 |
Multivariable |
Tourism Labour M5 |
SLOTH |
SLOTH: Structured Learning and Task-Based Optimization for Time Series Forecasting on Hierarchies |
None |
AAAI 2023 |
Multivariable |
Wind Solar |
eForecaster |
eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms |
None |
AAAI 2023 |
Multivariable |
NYCTaxi PEMS04 |
AutoSTL |
AutoSTL: Automated Spatio-Temporal Multi-Task Learning |
None |
AAAI 2023 |
Multivariable |
METR-LA PEMS-BAY |
Trafformer |
Trafformer: Unify Time and Space in Traffic Prediction |
None |
AAAI 2023 |
Multivariable |
Electricity PM2.5 Exchange |
DeLELSTM |
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/a8af7fffd7cace0f0ff9c61adaddc939667588662252fb2ff5e7b66ed0f7a00f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77616e67637130312f44654c454c53544d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2023 |
Multivariable |
NYC-Bike PEMS-BAY PEMS08 |
ReMo |
Not Only Pairwise Relationships: Fine-Grained Relational Modeling for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b27ed7fdfdf4065e610ec81fd8597debe67c3ed033c7383782184af4e08e01f7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626567696e6e65722d736b657463682f676d726c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2023 |
Multivariable |
NASA |
MetePFL |
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data |
Pytorch
![Forks](https://camo.githubusercontent.com/73568a91014f848fcf0fc31c49581ba1e815ab0090b8680ecdabf39a6e88f8a6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7368656e676368616f6368656e38322f4d65746550464c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2023 |
Multivariable |
Hurricane Climate |
Self-Recover |
Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning |
None |
IJCAI 2023 |
Multivariable |
Weather Traffc Electricity Exchange ILI |
SMARTformer |
SMARTformer: Semi-Autoregressive Transformer with Efficient Integrated Window Attention for Long Time Series Forecasting |
None |
IJCAI 2023 |
Multivariable |
METR-LA Beijing Xiamen |
INCREASE |
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging |
TF
![Forks](https://camo.githubusercontent.com/831e6d55ede7fa467072630e5cedb3829104960746ec5f6cc508b9af3b48a4d4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68656e67636875616e70616e2f494e4352454153453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2023 |
Multivariable |
MQPS ETT Electricity |
KAE-Informer |
KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices |
Pytorch
![Forks](https://camo.githubusercontent.com/1d45ab659a4ba790730b594e5c7192ed18ca1e9a7ea3039f411fcbe3d2b82be4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f636974736a7475323032302f4b41452d496e666f726d65722d4d5150533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2023 |
Multivariable |
Typhoon-JP COVID-JP Hurricane-US |
MemeSTN |
Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster |
Pytorch
![Forks](https://camo.githubusercontent.com/1d45ab659a4ba790730b594e5c7192ed18ca1e9a7ea3039f411fcbe3d2b82be4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f636974736a7475323032302f4b41452d496e666f726d65722d4d5150533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2023 |
Multivariable |
NYC Chicago |
EALGAP |
Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning |
Keras
![Forks](https://camo.githubusercontent.com/6fba0741974951ce8bcf944c7eae7c2a24415f6ac0e23dd7a05c3c88dedb0228/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f48756971756e4875616e672f45414c4741503f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2023 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
DyHSL |
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/926dddefab4f70dd3288b2cab4bf35da92c9f377a8fcc55d9d73fce4296a65dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59757368656e675a68616f2f447948534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2023 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
STWave |
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/17669089df2222620a07b02c24159bedae74ff9d1b4117d2232e730e50a7cc1e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c4d6973736865722f5354576176653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2023 |
Multivariable |
Seattle PEMS04 PEMS08 |
SSTBAN |
Self-Supervised Spatial-Temporal Bottleneck Attentive Network for Efficient Long-term Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/dacd2b251aeae918544e776635ac361fb7ca2027d93e67107ad36f2ad55d02ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f73686e424a54552f53535442414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2023 |
Multivariable |
PEMSD4 PEMSD8 AirBJ TrafficSIP |
MGTF |
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework |
Author |
WSDM 2023 |
Multivariable |
METR-LA PEMS-BAY PEMS04 PEMS07 PEMS08 |
STAEformer |
Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/7198df1af1c5991b8884b85aae5147662a1ac6b11143a6bef8985dae031ec294/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f58445a68656c6865696d2f53544145666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Traffic |
PEMS03 PEMS04 PEMS07 PEMS08 |
TrendGCN |
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b9b3ebb59ba4c03a459cff82d12ea01a9a4196bcdb330e66a927590ffd8f7c90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a75796f6e676a69616e672f5472656e6447434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
ETT Electricity Traffic Weather ILI Exchange |
GCformer |
GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/49be8c11d2ee9d0ba2e2a5dc209b90d8b74634f7d52796d0dba233dd6a58a70c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59616e6a756e2d5a68616f2f4743666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
ETT Electricity Traffic |
Seq2Peak |
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/6540487e67d3116f704ea5c62b8c070d2c1402c400c86ede8f5eb03138ea0928/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68616e677a7731362f536571325065616b3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
PEMS04 PEMS07 PEMS08 NYC Crime CHI Crime |
CL4ST |
Spatio-Temporal Meta Contrastive Learning |
Pytorch
![Forks](https://camo.githubusercontent.com/828b44d6a74a447f8ba173f8fe36567a70ed053e401d8a349c08c8a84cc62328/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f484b5544532f434c3453543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
NYC Bike NYC Taxi |
MLPST |
MLPST: MLP is All You Need for Spatio-Temporal Prediction |
Author |
CIKM 2023 |
Multivariable |
TaxiBJ BikeNYC |
MC-STL |
Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/02291ef882e8c7f62e3b372e9c491d626e3377c77a0dc5e02ceb7c6a1f73979f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f436f64655a78362f4d4353544c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
PeMS Beijing Electricity COVID-CHI |
MemDA |
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation |
Pytorch
![Forks](https://camo.githubusercontent.com/57996d911795bd7da6cf8a62f2016c4afe988aa1a36ac103185167891e5d4581/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f557262616e5f436f6e636570745f44726966743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Cross City Traffic |
PEMS-BAY METR-LA Chengdu Shenzhen |
TPB |
Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank |
Pytorch
![Forks](https://camo.githubusercontent.com/9a4c0d0a05af529a914cc22175260cb37b3d94b7f73de106b1e70b0e5d60b779/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68796c697530302f5450423f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Traffic Speed |
METR-LA PEMS-BAY PEMSD7M |
UAGCRN |
Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis |
TF
![Forks](https://camo.githubusercontent.com/264165257730698f8cc045c697f7d3386b6f9c8420f9ebf520babb9724a79d0d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f53756d696e48616e2f547261666669632d55414743524e54463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
Complaint NYC Taxi |
PromptST |
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/cd58234f36d734ecffc047edbecc0d21380f9d5fb87bb82b0f60ff86ba861fa2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5a68616e672d5a696a69616e2f50726f6d707453543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
METR-LA PEMS-BAY PEMS08 |
HIEST |
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/bc8bc82e7fb5eb2549f0bfd5d7ae292167285336bb1125791d727c979fbe85e8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f56414e2d5149414e2f43494b4d32332d48494553543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
ETT Electricity Weather Traffic |
TemDep |
TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/4f50fd67659c2d94877473ed8c0ea2ddd4eec1a11dabe9a9e205c9080990f041/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a6976676f676f676f2f54656d4465703f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Traffic |
BJ-Center METR-LA |
ST-MoE |
ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction |
None |
CIKM 2023 |
Multivariable |
ETT Electricity Weather Traffic Exchange |
AVGNets |
Learning Visibility Attention Graph Representation for Time Series Forecasting |
None |
CIKM 2023 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
STGBN |
Spatial-Temporal Graph Boosting Networks: Enhancing Spatial-Temporal Graph Neural Networks via Gradient Boosting |
None |
CIKM 2023 |
Multivariable |
ETT Electricity Traffic ILI Exchange |
FAMC-Net |
FAMC-Net: Frequency Domain Parity Correction Attention and Multi-Scale Dilated Convolution for Time Series Forecasting |
None |
CIKM 2023 |
Cross City Traffic |
NYC Chicago Nashville |
CARPG |
CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation |
None |
CIKM 2023 |
Traffic |
SPEED FLOW |
CANet |
Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting |
None |
CIKM 2023 |
Multivariable |
ETT Exchange ILI Weather Electricity Traffic |
DSformer |
DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction |
None |
CIKM 2023 |
Multivariable |
Wufu |
MODE |
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data |
None |
CIKM 2023 |
Multivariable |
NYC |
MetaRSTP |
Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning |
None |
CIKM 2023 |
Multivariable |
SIP NYC METR-LA |
G2S |
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics |
None |
SDM 2023 |
Multivariable |
Solar PEMS-BAY Electricity |
ERL |
Time-delayed Multivariate Time Series Predictions |
None |
SDM 2023 |
Multivariable |
Weather2K |
Weather2K |
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations |
Weather2K
![Forks](https://camo.githubusercontent.com/7d02586e650973b14f916a4776444ef1d344d2009bf97192d8a6b53bcfd4712a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6279636e667a2f77656174686572326b3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AISTATS 2023 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
FiLM |
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/7a6e572cd9a217e075c6da4fe6726bd57c813ad4733fc007c8a0c9ff9b2f19e7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7469616e7a686f75323031312f46694c4d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
ETT Electricity Exchange Weather |
LaST |
Learning Latent Seasonal-Trend Representations for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/4d754ed482db1031288eb374c7989528e681c1a28d78e24bd5fc3d86bee55c7a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a687963732f4c6153543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
WaveBound |
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/020c2cb19de566fa30fea34d2ed3255d31da67891d2b6e63c2d7d3c415735fe5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f63686f7969303532312f57617665426f756e643f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
COVID-19 PEMS04 PEMS08 Temperature Bytom Wind |
ZFC-SHCN |
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting |
Future |
NeurIPS 2022 |
Multivariable |
ETT Traffic Solar Electricity Exchange PEMS03 PEMS04 PEMS07 PEMS08 |
SCINet |
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction |
Pytorch
![Forks](https://camo.githubusercontent.com/027159ad6c31b4e7e3a192582f9ab38b9f8e26b200f51f90ca5dc203d4152c50/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f637572652d6c61622f5343494e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
Electricity ETT Exchange ILI Traffic Weather |
NonstaTransformer |
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/6d4d6a2a29d0c7d514a11bfcd95e3624fe53381d4c736aa86e3f4ab49f4e2b58/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7468756d6c2f4e6f6e73746174696f6e6172795f5472616e73666f726d6572733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
Traffic Solar Electricity Exchange PEMS07(M) PEMS-BAY |
TPGNN |
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks |
Future |
NeurIPS 2022 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
DSTAGNN |
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/0153e7234a6aab5f3cbeaea5b0d0ba6d8afcf65e4a22cc7b13a6f558752c9e3f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f53594c616e323031392f44535441474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2022 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
FEDformer (EncDec, EnhancedFeature) |
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/09e05d5b8e0a851abf8a7433dc4656bb3766964594e27f937c6250178c1d3ead/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4d415a6971696e672f464544666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2022 |
Multivariable |
Traffic Electricity Wiki Sales |
DAF |
DAF-Domain Adaptation for Time Series Forecasting via Attention Sharing |
None |
ICML 2022 |
Multivariable |
Electricity Solar Fred MD KDD Cup |
TACTiS (Copulas, Trans) |
TACTiS: Transformer-Attentional Copulas for Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/af076ea733bce8f19d9f771581ac4f23a23796cc051deea0ee5a329d0b0d87ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736572766963656e6f772f7461637469733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2022 |
Multivariable |
French Electricity |
AgACI |
Adaptive Conformal Predictions for Time Series |
Python,R
![Forks](https://camo.githubusercontent.com/1d74e1e6cede53dffd95a8d8896fa035aee26db0c7b59dc235f0fde4d6846f21/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d7a61666672616e2f4164617074697665436f6e666f726d616c50726564696374696f6e7354696d655365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2022 |
Traffic Speed |
NAVER-Seoul METR-LA |
PM-MemNet (Mem,KNN) |
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/819e0b5100f1202ff99f976c2a3096795e2e1ddc1b56b31948d03922c71774cc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4879756e576f6f6b4c2f504d2d4d656d4e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
PEMS03 PEMS04 PEMS08 COVID-19,etc |
TAMP-S2GCNets (GCN,AR, Topological Features) |
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting |
Pytorch |
ICLR 2022 |
Multivariable |
ETT Electricity Weather |
CoST |
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/0b7948cc2a36019ba33af8b20c6597a72a702558fafef4fd7b86cb835676e793/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f436f53543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
Electricity Traffic M4 CASIO NP |
DEPTS |
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/d8b4786cda87a0cc22b7f9c5c0085668a68808d478704aee35152a349a79dfc1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77656966616e74742f44455054533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
ETT Electricity Wind App Flow |
Pyraformer |
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/3fe5b163bded8a320eb316f3e5833a69c386d7d358d59bc603ec460d80a222fe/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c697061792f50797261666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
ETT Electricity M4 Air Quality Nasdaq |
RevIN |
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift |
Pytorch
![Forks](https://camo.githubusercontent.com/baeca97c0a0d73cb47ceaf08156649c5eb14456be016b08b7a60fd50a849c72b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f74732d6b696d2f526576494e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
METR-LA PEMS-BAY PEMS04 PEMS08 |
D2STGNN |
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/3da3f207f0585260bbff56b95f243f0092cd4b5083cf241d2d321f3c65553b79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a657a68697368616f2f44325354474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
VLDB 2022 |
Multivariable |
METR-LA PEMS-BAY PEMS04 |
STEP |
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/e1d07d2b3dc7f11d67db8e3c50534fdda0ffed60eb8e120c1a8b75a2fab3b2f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a657a68697368616f2f535445503f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
Solar Electricity Exchange Wind NYCBike NYCTaxi |
ESG |
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/78d8adea09dbb56ced39b7f03f4c7fa444c1af90895d28c3ab07803efcb06059/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c69755a482d31392f4553473f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
METR-LA Solar Traffic ECG5000 |
VSF |
Multi-Variate Time Series Forecasting on Variable Subsets |
Pytorch,dgl
![Forks](https://camo.githubusercontent.com/0e69c9b0f1a05a6d27923751d37ade4ab1926cb78226379bfa0d40c61c63c6ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f676f6f676c652f7673662d74696d652d7365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
DC Bike DC Taxi |
CrossTReS |
Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting |
Pytorch,dgl
![Forks](https://camo.githubusercontent.com/03b94881078c1ff17cf89872df8c07cb2981add6a84c405a1dcc33507890b457/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4b4c343830352f43726f7373545265533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
ETT Weather Exchange Traffic Electricity |
Quatformer |
Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting |
MRA-BGCN Author None Code |
KDD 2022 |
Multivariable |
NYCBike NYCTaxi PEMS03 PEMS08 |
GMSDR |
MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/e1adfd66ec9d4a79192289af1ddc5880dbcdf12a511c098fdfc84cde2c61f61a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f64636c697539392f4d5344523f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
Hangzhou NYC |
DTIGNN |
Modeling Network-level Traffic Flow Transitions on Sparse Data |
Pytorch
![Forks](https://camo.githubusercontent.com/01da6102d62ab58186ce6e335caa956f471bc60bf6ec88705f7f35358719ec30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736861776c656e2f647469676e6e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
Temperature Cloud cover Humidity Wind |
CLCRN |
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/66341091e53a8a70f2fa9031bb29c2acb7a4560fc891ebde872e7fede810e0e8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f424952442d54414f2f434c43524e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2022 |
Traffic Flow |
PEMS03 PEMS04 PEMS07 PEMS08 PEMS07(M) PEMS07(L) |
STG-NCDE |
Graph Neural Controlled Differential Equations for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/34a829344a0a2cca1a810bfdc45e1e67df6b9c1dff34746e376264d7190973d2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a656f6e677768616e63686f692f5354472d4e4344453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2022 |
Traffic Flow |
GT-221 WRS-393 ZGC-564 |
STDEN |
STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/912fb47fb912c49102db5c0b40f52ec776b151036df1de08ea1bbb33fd99243c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4563686f2d4a692f535444454e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2022 |
Multivariable |
Electricity Traffic PEMS07(M) METR-LA |
CATN |
CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting |
None |
AAAI 2022 |
Multivariable |
ETT Electricity |
TS2Vec |
TS2Vec: Towards Universal Representation of Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/c1dbcfafd057ac61a9735041d37d198bb1d88b2ace8f5ab129ed325a85da9209/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7975657a686968616e2f7473327665633f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2022 |
Multivariable |
ETT Electricity Weather |
Triformer |
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version |
Pytorch
![Forks](https://camo.githubusercontent.com/72047f3a5c9bbe44ddfe503c27c3bf72a84eb971e528bf6636bcd7160c73940d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f72617a76616e6339322f747269666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2022 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
FOGS |
FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/f10cd40732661000e2b4bcc8d95ef173cdef27974fc0840d4bd0e14d5f3ae35a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6b6576696e2d7875616e2f464f47533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2022 |
Multivariable |
PEMS04 PEMS08 RPCM |
RGSL |
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/c1045a981206710f7ba9b884903803365c9f444f53b837bc10e7f7a6a1680516/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c697061792f5247534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2022 |
Multivariable |
Air Quality Parking |
DMGA |
Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention |
None |
IJCAI 2022 |
Multivariable |
YellowCab GreenCab Solar |
ST-KMRN |
Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data |
Author |
IJCAI 2022 |
Multivariable |
NYCTaxi NYCBike CHIBike BJTaxi Chengdu |
STAN |
When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters |
None |
IJCAI 2022 |
Multivariable |
Hurricanes Ausgrid Weather |
DeepExtrema |
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data |
Pytorch
![Forks](https://camo.githubusercontent.com/e318620f77900bf9da73d3fb5fc4298fe0bbdb9299542c175b4435a0f050b26d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67616c696231392f4465657045787472656d612d494a43414932322d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2022 |
Multivariable |
GoogleSymptoms Covid19 Power Tweet |
CAMul |
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/be8207d42576e5427cf941836a03dd0d8188526d1600f425087e824b8a5ea8ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4164697479614c61622f43414d756c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2022 |
Multivariable |
Electricity Stock |
MRLF |
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/5c1aedce5c9c9fe28d022541c22e103137cc940b826573849c28283cdd628655/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f434d4c462d6769742d6465762f4d524c463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2022 |
Multivariable Classification Forecasting |
MuJoCo Google Stock |
EXIT |
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/eab9e0a473b17d53d914aed11e11f1f88cc8309a78cb889a785e07ca29e35f12/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7368656f796f6e2d6a68696e2f455849543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2022 |
Traffic Flow |
PEMS03 PEMS04 PEMS07 PEMS08 |
ST-WA |
Towards Spatio- Temporal Aware Traffic Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/c6894aeb67c140ac4a93b3417d2dcc3c01c7ccd7e3368cddbc6b850272ce17e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f72617a76616e6339322f53542d57413f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2022 |
Mobility Prediction |
NYC Dallas Miami |
SHIFT |
Translating Human Mobility Forecasting through Natural Language Generation |
Hao Xue |
WSDM 2022 |
Traffic Flow |
TaxiBJ BikeNYC |
ST-GSP |
ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/5f801868408dd6b63797145422a249dc95c8022d6d8b67c7ab0d3f765c5b0537/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6b35312f53544753503f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WSDM 2022 |
Multivariable |
Traffic Temperature |
ReTime |
Retrieval Based Time Series Forecasting |
None |
CIKM 2022 |
Multivariable |
Rainfall Traffic ETT Stock Climate |
DXtreMM |
Deep Extreme Mixture Model for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/8362f9008231683c76c732eb9114121d5b5e46992672d684e156cb7e3fdf815b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f44587472654d4d2f44587472654d4d5f5453463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2022 |
MTS Analysis MTS Forecasting Anormaly Detection |
ETT Electricity SMD SMAP MSL SWaT |
MARINA |
MARINA: An MLP-Attention Model for Multivariate Time-Series |
None |
CIKM 2022 |
Traffic Speed |
METR-LA PEMS-BAY |
ResCAL |
Residual Correction in Real-Time Traffic Forecasting |
None |
CIKM 2022 |
Model Selection |
|
AutoForecast |
AutoForecast: Automatic Time-Series Forecasting Model Selection |
None |
CIKM 2022 |
Traffic Flow |
PEMS04 PEMS07 PEMS08 |
DastNet |
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities |
Pytorch
![Forks](https://camo.githubusercontent.com/d57afee7275132e4ffbadbe5f1466215067c60aeaadcf0179ce0396d52883bc0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5969686f6e67542f444153544e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2022 |
Traffic Flow & Speed |
METR-LA PEMS-BAY PEMS03 PEMS04 PEMS07 PEMS08 |
AutoSTS |
Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction |
YongLi THU |
CIKM 2022 |
Traffic Condition |
TRCV-BJ TRCV-SH TRCV-ZZ |
DuTraffic |
DuTraffic: Live Traffic Condition Prediction with Trajectory Data and Street Views at Baidu Maps |
None |
CIKM 2022 |
Multivariable |
ETT Electricity WTH Weather ILI Exchange |
Linear |
Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting? |
None |
CIKM 2022 |
Multivariable |
Solar Traffic Electricity Exchange |
MAGL |
Memory Augmented Graph Learning Networks for Multivariate Time Series Forecasting |
None |
CIKM 2022 |
Multivariable |
PEMS04 PEMS07 PEMS08 PEMS-BAY Electricity |
STID |
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/603dfb2d86850f67f130851bb626ef03137fcd8b941aa8094336ffb3b7e7bf36/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a657a68697368616f2f535449443f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2022 |
Multivariable |
METR-LA PEMS-BAY PEMS04 PEMS07 |
ASTTN |
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting |
None |
CIKM 2022 |
Multivariable |
Seoul |
CGAN |
Context-aware Traffic Flow Forecasting in New Roads |
None |
CIKM 2022 |
Traffic Flow & Speed |
METR-LA PEMS-BAY PEMS-M PEMS04 PEMS08 |
ST-GAT |
ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction |
Author |
CIKM 2022 |
Traffic Speed |
METR-LA PEMS-BAY |
HOMGNNs |
Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/bc0ccac513cb84bee39557188cfc90e753a3cb3ae9d12e16feae2b1b22c18b93/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d61697375697169616e78756e2f484f4d474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2022 |
Multivariable |
M4 Electricity car-parts |
TopAttn |
Topological Attention for Time Series Forecasting |
Pytorch
Future |
NeurIPS 2021 |
Multivariable |
Rossmann M5 Wiki |
MisSeq |
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data |
None |
NeurIPS 2021 |
Multivariable |
ETT Electricity Exchange Traffic Weather ILI |
Autoformer |
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/84916e5500af2772b3ec21d26290d0136e294a6538de235b0cfff59d775f6599/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7468756d6c2f4175746f666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2021 |
Multivariable |
PEMS04 PEMS08 Traffic ADI M4 ,etc |
Error |
Adjusting for Autocorrelated Errors in Neural Networks for Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/a2f0dda10a74a6505d0c56cff119c33fc1e6b4d1b25e1457fcc2162ba1dce7c7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4461696b6f6e2d53756e2f41646a7573744175746f636f7272656c6174696f6e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2021 |
Multivariable |
Bytom Decentraland PEMS04 PEMS08 |
Z-GCNETs |
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b7b9587c000fb68a2541ef6b15c57a9cf45e82b9a60117de988417442a910413/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5a2d47434e4554732f5a2d47434e4554733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2021 |
Multivariable |
PEMS07(M) METR-LA PEMS-BAY |
Cov |
Conditional Temporal Neural Processes with Covariance Loss |
None |
ICML 2021 |
Multivariable |
METR-LA PEMS-BAY PMU |
GTS |
Discrete Graph Structure Learning for Forecasting Multiple Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/cbdb0cab80f43a2c7af65c63f49a7087423dc41dc2c164f05a7a6fe7a265d2c1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6368616f7368616e6763732f4754533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2021 |
Multivariable |
Benz Air Quality FuelMoisture |
framework |
A Transformer-based Framework for Multivariate Time Series Representation Learning |
Pytorch
![Forks](https://camo.githubusercontent.com/fe14c2fe2a3b9dcccc10cf9da038034148a14b561c30ee33385cb32a3944bd61/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f677a6572766561732f6d7674735f7472616e73666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2021 |
Federated Multivariable |
PEMS-BAY METR-LA |
CNFGNN |
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling |
Pytorch
![Forks](https://camo.githubusercontent.com/6a77912d93dcffd821a6ed4293f638516268610d1c20a02da9cbc17cc4e74990/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d656e67637a31332f4b4444323032315f434e46474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2021 |
Traffic Speed |
PEMS04 PEMS08 England |
DMSTGCN |
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/f2625ae148fdee8db1f029aa126829d39ddc41503a00f29a8e11088e9ab923d9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6c69616e677a686568616e2f444d535447434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2021 |
Traffic Flow |
PEMS07(M) PEMS07(L) PEMS03 PEMS04 PEMS07 PEMS08 |
STGODE |
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/82b91463d7d97d09a2c19083006a71942a4dab11bd8374647f77d0d9b71dac81/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7371756172652d636f6465722f5354474f44453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2021 |
Multivariable |
BikeNYC PEMS07(M) Electricity |
ST-Norm |
ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/21c888dca57b31c4afe02a43c0ecefb6262095a5c9f356eb5239fd201cfcc13c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4a4c44656e672f53542d4e6f726d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2021 |
Multivariable |
DiDiXM DiDiCD |
TrajNet |
TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction |
None |
KDD 2021 |
Robust Forecasting |
MIMIC-III USHCN KDD-CUP |
DGM |
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/ad5d98ee8adcea7449fbd402d043b017d53ba330c9ffc4c952ed7a1139956ee2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f746875777579696e6a756e2f44474d323f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
Guangzhou Seattle HZMetro , etc. |
DSARF |
Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/9dc9398b91ae4d54385cb2706f9d70227a12dd3a86bb9175e0993284fbf8a655/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6f7374616461626261732f44534152463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Speed |
METR-LA PEMS-BAY |
FC-GAGA |
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting |
TF
![Forks](https://camo.githubusercontent.com/657576a20b929982e106ce34732244f7d8b2f0c3d4c72c7d674bc24354433742/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626f726573686b696e61692f66632d676167613f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Speed |
DiDiJiNan DiDiXiAn |
HGCN |
Hierarchical Graph Convolution Network for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/d52de1a463c3f36d80e51d19d9a58188bf462c65badb383a8863aada5d676f65/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f6b616e3938372f4847434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
ETT Weather Electricity |
Informer |
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/8ef7eb33f0fbe4368c44bb571e31bd2e37874df7e807c5a8f53deea895ab3fdc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a686f7568616f79692f496e666f726d6572323032303f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Flow |
NYCMetro NYC Bike NYC Taxi |
MOTHER |
Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction |
None |
AAAI 2021 |
Multivariable |
METR-LA PEMS-BAY PEMS07(M) PEMS07(L) PEMS03 PEMS04 PEMS07 PEMS08 |
STFGNN |
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting |
Mxnet
![Forks](https://camo.githubusercontent.com/02b573095d5d164668b8f9c9039b3fa810b476bc2c1b31b5521c32db2ea760fd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4d656e677a68616e674c492f535446474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
BJ Taxi NYC Taxi NYC Bike1 NYC Bike2 |
STGDN |
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network |
Mxnet
![Forks](https://camo.githubusercontent.com/08be2e668b5ba53f2a9aaee3398240e590f4a98e26d8d9f1144048821b888465/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e696d696e676e696d696e672f67646e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Flow |
SG-TAXI |
TrGNN |
Traffic Flow Prediction with Vehicle Trajectories |
Pytorch
![Forks](https://camo.githubusercontent.com/0c7c3905700ccca264c7a46c25e5facf840c747e85f6d7664afeb3479778dedb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d696e677169616e3030302f5472474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
Road POIs SIGtraf |
DMLM |
Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach |
Future |
IJCAI 2021 |
Multivariable |
East Bay METR-LA US |
D-DA-GRNN |
EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/78ec3832a41b7c739cdec2ca9dca22392c3f7adde4e88fae2f6349eddf6c2445/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f72617a76616e6339322f456e68616e63654e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2021 |
Multivariable |
Water Humidity Wind, etc |
EA-DRL |
An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting |
None |
ICDE 2021 |
Traffic Flow |
TaxiBJ DiDiCD TaxiRome |
AttConvLSTM |
Modeling Citywide Crowd Flows using Attentive Convolutional LSTM |
None |
ICDE 2021 |
Traffic Speed Traffic Flow |
METR-LA PEMS-BAY eMS03 PEMS04 PEMS07 PEMS08... |
Benchmark |
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data |
Future |
ICDE 2021 |
Multivariable |
Motes Soil Revenue Traffic 20CR |
NET |
Network of Tensor Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/a314bb42156f7b7e0a54c0b77644bd5869780342661909e38997a3e0cde9dc42/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f62616f79756a696e672f4e4554333f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2021 |
Multivariable |
VevoMusic WikiTraffic LOS-LOOP SZ-taxi |
Radflow |
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/96051d5901e232de106a53f5a84092144900f8e7fea3408a71d9b3dc8c8e754d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c6173646169727472616e2f726164666c6f773f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2021 |
Multivariable |
METR-LA Wiki-EN |
REST |
REST: Reciprocal Framework for Spatiotemporal-coupled Predictions |
None |
WWW 2021 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 HZMetro |
ASTGNN |
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/aa2c421b6be0590ff5adc3b59ee32a1aac471a071a6a73a52db2e2acea6dff8e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f73686e424a54552f415354474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
TKDE 2021 |
Multivariable |
TaxiBJ BikeNYC-I BikeNYC-II TaxiNYC METR-LA PEMS-BAY PEMS07(M) |
DL-Traff |
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction |
Graph:PyTorch Grid:TF
![Forks](https://camo.githubusercontent.com/0c8cccbc7c441a6164200f896e2cfc4b9b8e66b6152557034e3a55a778f0191e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f444c2d54726166662d47726170683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2021 |
Multivariable |
METR-LA PEMS-BAY PEMS07(M) |
TorchGeoTem |
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models |
PyTorch |
CIKM 2021 |
Traffic Flow |
TaxiBJ BikeNYC |
LLF |
Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction |
None |
CIKM 2021 |
Multivariable |
ETT Electricity |
HI |
Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting |
None |
CIKM 2021 |
Multivariable |
ETT ELE |
AGCNT |
AGCNT: Adaptive Graph Convolutional Network for Transformer-based Long Sequence Time-Series Forecasting |
None |
CIKM 2021 |
Cellular Traffic |
cellular |
MPGAT |
Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic |
Pytorch
Future |
CIKM 2021 |
Traffic Speed |
METR-LA PEMS-BAY Simulated |
STNN |
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/cee5743025c4fda76e278b73ae5cdfc7f4e7079c29dfc6cc9e113ed43817656b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736f6e6779616e67636f2f53544e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2021 |
Traffic Speed |
DiDiCD DiDiXiAn |
T-wave |
Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/cee5743025c4fda76e278b73ae5cdfc7f4e7079c29dfc6cc9e113ed43817656b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736f6e6779616e67636f2f53544e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2021 |
Multivariable |
Sanyo Hanergy Solar Electricity Exchange |
SSDNet |
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/f92158c53441c58a8a03f663e8d0bb967f548dd7157adc7bc08710776fe5f241/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59616e674c494e313939372f5353444e65742d4943444d323032313f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2021 |
Traffic Volumn |
HangZhou City JiNan City |
CTVI |
Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference |
Pytorch
![Forks](https://camo.githubusercontent.com/1e71802f83e8c086f94861e05b3f650feb6243462420147860de95d6a40ff135/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f64736a39362f435456492d6d61737465723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2021 |
Traffic Volumn |
Uber Movements Grab-Posisi |
TEST-GCN |
TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting |
None |
ICDM 2021 |
Multivariable |
Air Quality City Meterology |
ATGCN |
Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction |
None |
WSDM 2021 |
Traffic Flow |
WalkWLA BikeNYC TaxiNYC |
PDSTN |
Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network |
None |
WSDM 2021 |
Traffic Flow |
PEMS04 PEMS08 |
AGCRN |
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/14c4aa96f37181681f0754dc8f729138538478c2fcab1cf6a946e345dc60c8da/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c65694241492f414743524e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2020 |
Multivariable |
Electricity Traffic Wind Solar M4-Hourly |
AST |
Adversarial Sparse Transformer for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/3ec47901279ce53a0c92659e253c15ff1a7b97d7a9ddd8208a2d6013f5f6d023/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68696869686968697773662f4153543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2020 |
Multivariable |
METR-LA PEMS-BAY PEMS07 PEMS03 PEMS04 ,etc |
StemGNN |
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/9213768ad9ba35e65b6e87c2d01944ca8c840257ddc3d4033475e6334e7282c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d6963726f736f66742f5374656d474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2020 |
Multivariable |
M4 M3 Tourism |
N-BEATS |
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting |
Pytorch+Keras
![Forks](https://camo.githubusercontent.com/df0f77012492ab5b94eef9f91793f1611a2af51e67afa759e12ad0795d7b3a2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7068696c6970706572656d792f6e2d62656174733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2020 |
Traffic Flow |
Traffic Energy Electricity Exchange METR-LA PEMS-BAY |
MTGNN |
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/bb3e062634d8bc20f39b3af4bb3157a0515ac9af89a88b99c527025fef165003/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6e7a68616e2f4d54474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2020 |
Traffic Flow |
Taxi-NYC Bike-NYC CTM |
DSAN |
Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction |
TF
![Forks](https://camo.githubusercontent.com/c334b02dd091b23432bb0384bf7f4af1c4eb3bd16872e4d1d577bf42cd081883/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68616f78696e676c2f4453414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2020 |
Traffic Speed Traffic Flow |
Shenzhen |
Curb-GAN |
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/fcf7eec43c6b97da94b4a80d6bb9106a5ca54b707ef2f69d96d4e81a2e6a5c01/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f437572622d47414e2f437572622d47414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2020 |
Traffic Flow |
TaxiBJ CrowdBJ TaxiJN TaxiGY |
AutoST |
AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction |
None |
KDD 2020 |
Traffic Volumn |
W3-715 E5-2907 |
HSTGCN |
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data |
None |
KDD 2020 |
Multivariable |
Xiamen PEMS-BAY |
GMAN |
GMAN: A Graph Multi-Attention Network for Traffic Prediction |
TF
Pytorch |
AAAI 2020 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
STSGCN |
Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting |
Mxnet
Pytorch
![Forks](https://camo.githubusercontent.com/a174398778b530da0db8574a613e9c782dcce61bd22e8fbe168f7f33b2e7cdb5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f536d616c6c4e616e612f53545347434e5f5079746f7263683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
Traffic Energy NASDAQ |
MLCNN |
Towards Better Forecasting by Fusing Near and Distant Future Visions |
Pytorch
![Forks](https://camo.githubusercontent.com/d9320405a9d537143acf7765cedc5754abb88c0246e9bd6810e7f1e9f2a12d3c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736d616c6c47756d2f4d4c434e4e2d4d756c7469766172696174652d54696d652d5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
PEMS-S PEMS-BAY METR-LA BJF BRF BRF-L |
SLCNN |
Spatio-temporal graph structure learning for traffic forecasting |
None |
AAAI 2020 |
Traffic Speed |
METR-LA PEMS-BAY |
MRA-BGCN |
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting |
None |
AAAI 2020 |
Metro Flow |
HKMetro |
WDGTC |
Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction |
TF
![Forks](https://camo.githubusercontent.com/df6e0cf9f4689acc574097772b2ff905b44fea6982d998d190e14abf770d0f3c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626f6e616c646c692f5744475f54433f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
MovingMNIST TaxiBJ KTH |
SA-ConvLSTM |
Self-Attention ConvLSTM for Spatiotemporal Prediction |
TF
PyTorch |
AAAI 2020 |
Metro Flow |
SydneyMetro |
MLC-PPF |
Potential Passenger Flow Prediction-A Novel Study for Urban Transportation Development |
None |
AAAI 2020 |
Commuting Flow |
Lodes Pluto OSRM |
GMEL |
Learning Geo-Contextual Embeddings for Commuting Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/fa2651cd23d2aba0656c8583ed868aa50a967cc252f4ea4562a6cf2a41d43582/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a61636b6d69656d69652f474d454c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
Traffic Exchange Solar |
DeepTrends |
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series |
TF
![Forks](https://camo.githubusercontent.com/39c26d22408e71fc2ccd2aca571cbf349773c59cc65e231829c8d7e853a7070a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f446572726f6e58752f446565705472656e64733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
Traffic Electricity SmokeVideo PCSales RawMaterials |
BHT |
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting |
Python
![Forks](https://camo.githubusercontent.com/53a8ae2dce6e67ca4ab1c9793a40e641de7e3a9bd150d02a48f732a80e74c4f3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6875617765692d6e6f61682f4248542d4152494d413f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Traffic Speed |
PEMS04 PEMS07 PEMS08 |
LSGCN |
LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks |
TF
![Forks](https://camo.githubusercontent.com/a4cd0678430f780824a4780e789aa10d6553709b0c38b013d7a0a420de1fbedf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68656c616e7a68752f4c5347434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2020 |
Traffic Flow |
BikeNYC MobileBJ |
CSCNet |
A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling |
None |
IJCAI 2020 |
Multivariable |
USDCNY USDKRW USDIDR |
WATTNet |
WATTNet: learning to trade FX via hierarchical spatio-temporal representation of highly multivariate time series |
TF
![Forks](https://camo.githubusercontent.com/937c0ecc52e48f35445a3efcae9fbb60022dc516806bc53160090a40451412e9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7061626c6f766963656e74652f6b657261732d776174746e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2020 |
Fine-grained |
CitiBikeNYC Div Metro |
GACNN |
Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems |
None |
WWW 2020 |
Flow Distribution |
Austin Louisville Minneapolis |
GCScoot |
Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration |
None |
WWW 2020 |
Traffic Speed |
METR-LA PEMS-BAY |
STGNN |
Traffic Flow Prediction via Spatial Temporal Graph Neural Network |
Pytorch
![Forks](https://camo.githubusercontent.com/c8bfbc5a79aa060a173feab23a86dffb64eb64f4b22a81300d01df30c4f134fd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c4d6973736865722f5354474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2020 |
Traffic Speed |
DiDiCD |
STAG-GCN |
Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/607a18d102540bc5bd1dddba81b71119b0377449dec938ca6b032a4472faddb7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f526f62696e4c75313230392f535441472d47434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2020 |
Traffic Speed |
METR-LA PEMS-BAY |
ST-GRAT |
ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed |
Pytorch
![Forks](https://camo.githubusercontent.com/67e7b642f89d3921929e4d2d32c17b363189cbff9c4d0b4889ff4b082b03a02f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c4d6973736865722f53542d475241543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2020 |
Traffic Flow |
BJ-Taxi NYC-Taxi NYC-Bike-1 NYC-Bike-2 |
ST-CGA |
Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting |
Keras
![Forks](https://camo.githubusercontent.com/4dd3112b515718524c2bec20db73d03d931a888e1a32aba9d502e7e56b04debc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a62646a2d737461722f6367613f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2020 |
Traffic Flow |
NYCBike NYCTaxi |
MT-ASTN |
Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/76b5c21eae673d84c0c309ccef396362910c749d4a7ce529795cda84bfd12f55/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4d69616f48616f53756e6e792f4d542d4153544e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2020 |
Traffic Speed |
SFO NYC |
DIGC |
Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction |
None |
CIKM 2020 |
Metro Flow |
SZMetro HZMetro |
STP-TrellisNets |
STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction |
None |
CIKM 2020 |
Multivariable |
Air Quality BikeNYC METR-LA |
AGSTN |
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting |
Keras
![Forks](https://camo.githubusercontent.com/a4b1315a9d8a2af0cf36bf203e311a5bf9d52450edab2505c2c2b5c05176c73f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6c3835323838382f414753544e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2020 |
Traffic Speed |
METR-LA PEMS-BAY |
FreqST |
FreqST: Exploiting Frequency Information in Spatiotemporal Modeling for Traffic Prediction |
None |
ICDM 2020 |
Traffic Flow |
PEMS03 PEMS07 |
TSSRGCN |
Tssrgcn: Temporal spectral spatial retrieval graph convolutional network for traffic flow forecasting |
None |
ICDM 2020 |
Multivariable |
Air Quality DarkSky Geographic |
DeepLATTE |
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/67f66c2d049beeaec8f84d4588a2215ce75c7fd85d2efe6b27789acd0fa469b7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7370617469616c2d636f6d707574696e672f646565706c617474652d66696e652d7363616c652d70726564696374696f6e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2020 |
Traffic Flow |
XATaxi BJTaxi PortoTaxi |
ST-PEFs |
Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields |
None |
ICDM 2020 |
Traffic Speed Flow |
SZSpeed SZTaxi |
cST-ML |
cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/09e11a06dc2f8d8c79c6ad917181d5098166a9b3b14b4c002b8959bff3496e58/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f79696e677875652d7a68616e672f6353542d4d4c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2020 |
Multivariable |
Electricity Traffic Wiki PEMS07(M) |
DeepGLO |
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/16cbeebaa29f65688fb86f80f236292e0e99b3c14af8468d55499cb71e1aaf38/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f72616a617473656e39312f64656570676c6f3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2019 |
Multivariable |
Electricity Traffic Solar M4 Wind |
LogSparse |
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/7905d2ce3074578e036d6fd3db378fd347fa0cdee58f0f8f2d4ac550f23cf38e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d6c706f747465722f5472616e73666f726d65725f54696d655f5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2019 |
Multivariable |
Synthetic ECG5000 Traffic |
DILATE |
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models |
Pytorch
![Forks](https://camo.githubusercontent.com/c3bceffa083f499dbf7c0f1dca625b14a72ebb202828fe09e5a360a0be4383e6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f76696e63656e742d6c656775656e2f44494c4154453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2019 |
Traffic Flow |
Earthquake |
DeepUrbanEvent |
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events |
Keras
![Forks](https://camo.githubusercontent.com/6ca347ec45e603965d9814ba58bfa768d81f30d19e45470b1cee2b9c9c0630c5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b6173686977612f44656570557262616e4576656e743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2019 |
Traffic Flow Speed |
TDrive METR-LA |
ST-MetaNet |
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning |
Mxnet
![Forks](https://camo.githubusercontent.com/6e6393967a9e1957487d11c27317f3a528f018749a901b7deedf2e6eb45c01c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f70616e7a686579692f53542d4d6574614e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2019 |
Multivariable |
Rossman Walmart Electricity Traffic Parts |
ARU |
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units |
TF
![Forks](https://camo.githubusercontent.com/3dcc77ada28571b0d13198b480fc8835cb7a49347f1f3a52d879e08381aa6ccd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7072617468616d31366373652f4152553f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2019 |
Multivariable |
Air Quality |
AccuAir |
AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 |
None |
KDD 2019 |
Traffic Flow |
Simulated RoadTraffic Wikipedia |
ERMreg |
Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions |
None |
KDD 2019 |
Multivariable under event |
Climate Stock Pseudo |
EVL |
Modeling Extreme Events in Time Series Prediction |
None |
KDD 2019 |
Traffic Flow |
PEMS04 PEMS08 METR-LA |
ASTGCN |
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
Mxnet
![Forks](https://camo.githubusercontent.com/a55404349580ad8c904439adaa3f87d649a2a3ecea5f2d55aaecaa724a172c23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f446176696468616d332f41535447434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2019 |
Traffic Flow Speed |
NYC PEMS0(M) |
DGCNN |
Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting |
None |
AAAI 2019 |
Traffic FLow |
NYC-Taxi NYC-Bike |
STDN |
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction |
Keras
![Forks](https://camo.githubusercontent.com/b3f37d216f51ca13743892b80e45fb186be272f2a8407d12bd0515e1859ed18c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f74616e677869616e66656e672f5354444e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2019 |
Traffic Flow |
MobileBJ BikeNYC |
DeepSTN+ |
DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis |
TF
![Forks](https://camo.githubusercontent.com/d1bd12b446b4be8d6876f90a4f667e426f1f75a15e84c5fb5646628fad378663/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7473696e676875612d6669622d6c61622f4465657053544e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2019 |
Traffic Speed |
METR-LA PEMS-BAY |
Res-RGNN |
Gated residual recurrent graph neural networks for traffic prediction |
None |
AAAI 2019 |
Traffic FLow |
MetroBJ BusBJ TaxiBJ |
GSTNet |
GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/d5b9304cd98c09f2655693f6e2073fd4360c99b5069df099abbf1e0eb47d80ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f576f6f6453756761722f4753544e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2019 |
Traffic Speed |
METR-LA PEMS-BAY |
GWN |
Graph WaveNet for Deep Spatial-Temporal Graph Modeling |
Pytorch
![Forks](https://camo.githubusercontent.com/c0858874ad925e76c79f9a56bd7096cff60b7e78298e3e672dabd7e91fc5d152/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6e7a68616e2f47726170682d576176654e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2019 |
Traffic Flow |
DidiSY BikeNYC TaxiBJ |
STG2Seq |
STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting |
TF
![Forks](https://camo.githubusercontent.com/947ab2c056da9416931c9e4eb261ca8f1b41127f9cfaf96b0a18e8f963d12123/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c65694241492f535447325365713f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2019 |
Multivariable |
GHL Electricity TEP |
DyAt |
DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems |
Pytorch
![Forks](https://camo.githubusercontent.com/f408845c574cf4db37b61c20297108d9aa1c63d8ddbdcba805fc1dd22e4a9d51/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6d7572616c6964312f44796e616d6963417474656e74696f6e4e6574776f726b733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2019 |
Multivariable |
Air Quality |
MGED |
Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction |
None |
IJCAI 2019 |
Traffic Volumn |
Chicago Boston |
MetaST |
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction |
TF
![Forks](https://camo.githubusercontent.com/3eb8dee9d02ecca2bbc82a99c991cd1c638139956301dc5cc97124f447988f23/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68756178697579616f2f4d65746153543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2019 |
TrafficPred imputation |
GZSpeed HZMetro Seattle London |
BTF |
Bayesian Temporal Factorization for Multidimensional Time Series Prediction |
Python
![Forks](https://camo.githubusercontent.com/f408845c574cf4db37b61c20297108d9aa1c63d8ddbdcba805fc1dd22e4a9d51/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6d7572616c6964312f44796e616d6963417474656e74696f6e4e6574776f726b733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
TPAMI 2019 |
Multivariable |
Gas Station |
DSANet |
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/d23a00a8f8cfe8b3090accc38ca8ccbab4bd28f90511c923a82f6eb4170aaddf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6269676875616e673632342f4453414e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2019 |
Multivariable |
Solar Traffic Exchange Electricity PEMS ,etc |
Study |
Experimental Study of Multivariate Time Series Forecasting Models |
None |
CIKM 2019 |
Traffic Speed |
DiDiCD DiDiXA |
BTRAC |
Boosted Trajectory Calibration for Traffic State Estimation |
None |
ICDM 2019 |
Multivariable |
Photovoltaic |
MTEX-CNN |
MTEX-CNN: Multivariate Time Series EXplanations for Predictions with Convolutional Neural Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/2ab70b2cd3842d13cf713d72f3d9c0467ea1cd398a85f73c0f86868815ca1fcb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f647579616e687068616d2d6272732f5841492d4d756c7469766172696174652d54696d652d5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2019 |
Traffic Speed |
BJER4 PEMS07(M) PEMS07(L) |
STGCN |
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting |
TF
Mxnet Pytorch1
Pytorch2 Pytorch3
![Forks](https://camo.githubusercontent.com/e123b6a1f6a4512d6a578ab604a997ada04b5d6632e5bf4e434811cf774aa8dc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f416775696e2f535447434e2d5079546f7263683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2018 |
Traffic Speed |
METR-LA PEMS-BAY |
DCRNN |
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting |
TF
Pytorch |
ICLR 2018 |