Paper Nums:100+ |
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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/2837f60fdb7b1697f0f2662ae76cbe2eb81e7d7725cf619ae5d3b2a829a4b8db/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f4465657054696d653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2023 |
Multivariable |
Synthetic Taxi Electricity Traffic |
FeatureP (Feature Enhancement) |
Feature Programming for Multivariate Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/188d1df757f3e068afd1bb74e19c7e3c55ef262c2ccee9af28216adb825c51bb/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/73f927c5c559774636770cb9fec0a44c0fe02e544f2d74fb311e2192a72340f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77616e67687132312f4d49434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/c4b5653a056d79ac90b53dc67da2865f996c334beded14ab1159bc0fa7ac675c/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/b80acd735fee479e49ab5c9f293edda3bcb102d7f3d5111ab4da5815c9dc853f/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/63f71bedff7b2238c1488baaa3039fbe9ddfd6925b80bbde679721a39fce3c89/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a6f686e2d782d6a69616e672f6d6574615f73736d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
ETT Electricity Traffic Weather |
FSNet |
Learning Fast and Slow for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/40473d419900ec52a354333275ed833e8295c0e77c093a4ef7c311e5a0f53971/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f66736e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/534eeea22b973b7541f44a0cc95dc001210753f9bedc66943f167a58ac3e3e69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f676f6f676c652d72657365617263682f676f6f676c652d72657365617263682f747265652f6d61737465722f4b4e463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2023 |
Multivariable |
ETT Weather Electricity Traffic Exchange |
SpaceTime |
Effectively Modeling Time Series with Simple Discrete State Spaces |
Pytorch
![Forks](https://camo.githubusercontent.com/158511b355b56053ebb788c98ee7106b948851342387a4fdbf77902fd5d0d085/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f48617a7952657365617263682f737061636574696d653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/b22f5cba52ef6206a6d56fad2fb5e55c4f3097e32bbabe62b422396a5eed5ecf/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/c6f6f78148fb529b745579eb54621f3c7eba920e20916f185f6d57cb438254e5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f426f7265616c697341492f7363616c65666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/fdf3d9eb0388f8d659c7caa5d7b9d178ef2a4d03dc11c8874f01aad4774b3032/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d617474676f72622f7370617273652d62696e6172792d7472616e73666f726d6572733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/54b993894ff07521887ae862cfb8b34611734e1c7eebb00a1b3a0b5a65056f9f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a7a7979303932392f4b444432332d4361755354473f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Robust Multivariable |
PEMS-BAY PEMS04 |
RDAT |
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training |
Pytorch
![Forks](https://camo.githubusercontent.com/e0295bbb880f701635d7b18411c0b0632a21f9b125727dc425fefb036bf341b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f757361696c2d686b7573742f524441543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2023 |
Multivariable |
Beijing Chengdu Harbin |
Frigate |
Frigate: Frugal Spatio-temporal Forecasting on Road Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/2b1a20300ebef188c8c0e1a29f8b2bd869988951f2ce7437cdd4ba58753ff782/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f696465612d696974642f667269676174653f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/5ebc88da41c5104ad73b04e2c28e3f51bb3d7d90d483f3c56b2e1559130c0497/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4164697479614c61622f50726f666869743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/93f605c94c6ce7e2c09785b4580204ec58addfd84b5ace7f4d06029668680205/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f637572652d6c61622f4c5453462d4c696e6561723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/4b482c1d9ea7cda540b91eadee14763ef032cb3dcabcdfd07c1426bc53d2ec25/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f557262616e2d436f6d707574696e672f5354432d44726f706f75743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
BJ-Bike NYC-Bike |
STNSCM |
Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/d461b89b442955b73013e2096140e6f843e0644e724bae819c154e4f13b1efb2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f457465726e6974795a592f53544e53434d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
XC-Trans XC-Speed |
CCHMM |
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/89bdf00bf6ccaee26c9a3cbc78f8a203aab411febb3b3e25cb29a0f18f028584/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f457465726e6974795a592f4343484d4d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
NYCBike1 NYCBike2 NYCTaxi BJTaxi |
ST-SSL |
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/6bfdad7770d50115fb7b33da646c29692dab6adf1ca5a0c9762a5c9fe9868200/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4563686f2d4a692f53542d53534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
PV-US CER-En |
SGP |
Scalable Spatiotemporal Graph Neural Networks |
Pytorch
![Forks](https://camo.githubusercontent.com/b9d3aa4d08c4cd89cf05a3bbd5f644f00024787a5d4373ecfe8d26d425f34880/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f47726170682d4d616368696e652d4c6561726e696e672d47726f75702f7367703f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/54c51f14025a619ca427001824476389f52f2238a964b9906720e5e8387d82bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4172746875722d4e756c6c2f5352443f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
ETT Electricity |
InfoTS |
Time Series Contrastive Learning with Information-Aware Augmentations |
Pytorch
![Forks](https://camo.githubusercontent.com/6d914bd066b9cf664ac50e1e5f5d5b903fc194927297e663aafec6fbb3616fb1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6368656e677730372f496e666f54533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
PhysioNet MIMIC-III Activity Appliances Energy |
PrimeNet |
PrimeNet: Pre-training for Irregular Multivariate Time Series |
Pytorch
![Forks](https://camo.githubusercontent.com/73eaad00d20a853b3beb92a4aba83889983595da6f72064d59e1bb193320262d/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/3d9fff006972c4970758561a489e7348066fde1af93a4026d3cb750c7fa21f8e/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/bf66a97f2cab37d78298a95c5022905b813bf04dcb37c0c11d2937a9db73b3e1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4e6978746c612f6e657572616c666f7265636173743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2023 |
Multivariable |
METR-LA ETT Weather |
MegaCRN |
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/cf341dfdd8393f743cf4b31ebe3d023d432276ea1305dafd5a690bc6227dd1a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f4d65676143524e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/fd6de6f7af746813e20c855832f115d9729fd0c7e21a9306a8ec290575402b04/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6461766964616e617374617369752f4e4543506c75733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/9e2cb84d85db04405c84ad7a574527061f31c7a33aa8123c09e2f7c2ccf66b51/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c616e796f756e67434e2f57617665466f724d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/36771f4ee57db8232f77761f654c02ce6abf7f68e52718c1895b7e92c31dd94b/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/5b9a874ab80285a3153a40b1c36ddcd8d845389270df506f84e3e3030224f1bb/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/8709372c9a94ac804f54d4e285dcd6d7ce425b1846af998b7feaa69556e2453b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626567696e6e65722d736b657463682f676d726c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2023 |
Multivariable |
NASA |
MetePFL |
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data |
Pytorch
![Forks](https://camo.githubusercontent.com/19d2f06ae9fcaa547ab8257c703f70c0f01a127fc7aadf70417b173afd302d92/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7368656e676368616f6368656e38322f4d65746550464c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/24a997c277cf66e4f61e24070fbbce25056f689233fc9ce0ea2cbf62c13935f1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68656e67636875616e70616e2f494e4352454153453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/4d6f7372af93293fdc424dccd199f76d22dd4caf25d809b3ec0d084d8e869961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f636974736a7475323032302f4b41452d496e666f726d65722d4d5150533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/4d6f7372af93293fdc424dccd199f76d22dd4caf25d809b3ec0d084d8e869961/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f636974736a7475323032302f4b41452d496e666f726d65722d4d5150533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2023 |
Multivariable |
NYC Chicago |
EALGAP |
Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning |
Keras
![Forks](https://camo.githubusercontent.com/3eb1864c0df23e0b1160adff05557df8ce09795878c7beef685d635976544661/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f48756971756e4875616e672f45414c4741503f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDE 2023 |
Multivariable |
PEMS03 PEMS04 PEMS07 PEMS08 |
DyHSL |
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/2eaeed996f5a19fe54f2414452dd7202a4706e09b4d5b9ee10af6df259a2f200/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59757368656e675a68616f2f447948534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/ccb9324c93c2620a111259f4b3bfd9c048657d3f2b45e15ce08d24625b0f782a/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/10f0dbd26b34a9f21cf34e1582f0ad07194bbb39d732a422fb989f7cb1c48bc7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f73686e424a54552f53535442414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/26600fff54af4298d928fd366bd7d3d17250f69c65c2eee79cc5b162c1f9d221/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f58445a68656c6865696d2f53544145666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/f30d2c87b906b908f8f3807f5dd28e874f4e7810cc0af562ae6cc8a76fef982f/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/f4e858a5db92ed78b69f28c0b96f5b5b7bb44b21e65e1fa1d06df27b977f6ab1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59616e6a756e2d5a68616f2f4743666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
ETT Electricity Traffic |
Seq2Peak |
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/bab0d91364c6c952f7f59c123dfcb5dcfc214f2930f1e7242e9226efde5d5b89/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68616e677a7731362f536571325065616b3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
PEMS04 PEMS07 PEMS08 NYC Crime CHI Crime |
CL4ST |
Spatio-Temporal Meta Contrastive Learning |
Pytorch
![Forks](https://camo.githubusercontent.com/89f540f36bdd9483f05f8582f3d6c892615debcb5d254277afd1426c891a7af4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f484b5544532f434c3453543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/595d1ae995666c0f0fa577ca0d69f48686b12aaa0a04426b3250235b06a26f6e/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/2b3de228f8bde3bac48bbb631d1094cf8bacb1463d2e2dc12f25cf290543c1e9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f557262616e5f436f6e636570745f44726966743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/5ff0a75f475a38890d3eec04baea0f22133f89db1c2db02aaaaf105d7694f738/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a68796c697530302f5450423f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/e07bb30de4c248d84a07a57713865f6699a1bc18cc120ac701c64d944ae12dd2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f53756d696e48616e2f547261666669632d55414743524e54463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
Complaint NYC Taxi |
PromptST |
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/526c5e50e48fc9927a693e8919e79a7f3bac75774f5d79a56fa411462c6bbf3e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5a68616e672d5a696a69616e2f50726f6d707453543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
METR-LA PEMS-BAY PEMS08 |
HIEST |
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/6d077ddc45469262316a8d1ff54e0a758285de446cba21b4fb3e359c191c7184/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f56414e2d5149414e2f43494b4d32332d48494553543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
CIKM 2023 |
Multivariable |
ETT Electricity Weather Traffic |
TemDep |
TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/e39c00975c35e23ef8cb58851d265f282fa85b584cc022b637072369c21de2a7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a6976676f676f676f2f54656d4465703f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/925ec26163624956e9526bd98991f4c443fee411d67d34e7e3bd3045e29cfc6b/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/7674d955c28b9c2f3e832a3c3c802ef97d8542ba4d8aa8bc85eb1ab945fbcd22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7469616e7a686f75323031312f46694c4d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2022 |
Multivariable |
ETT Electricity Exchange Weather |
LaST |
Learning Latent Seasonal-Trend Representations for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/8edd2a9f08f713aa9aef732163c3f4caf6478e016bd1db4cfcae51d79dbaedee/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/586890b9d4254d87eb7dc6cc43076c8c2ee3a2a6f2bdbade5b58689c6d0f2dba/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/6de297f41b87a35c7ebd3431a0cd0bf8285d1b986aece134389922d766f5eeb2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f637572652d6c61622f5343494e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/85a549d089a3a55f29f47c700524a2dca77c7cf6a1a21e59831eabd6861ff05c/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/27006c3f2129236accaeb924ba80a3b31ada284e8222b7a9f7ea5899175a1672/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f53594c616e323031392f44535441474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/4fc71371f5a4617ed468d658be14f014a7ba2637271814921a70c9e3e57ff5d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4d415a6971696e672f464544666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/01abc54c4826e197368ba3a8251e1fe55479ce3078ff46ea781c78bfdf7dfbfc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736572766963656e6f772f7461637469733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICML 2022 |
Multivariable |
French Electricity |
AgACI |
Adaptive Conformal Predictions for Time Series |
Python,R
![Forks](https://camo.githubusercontent.com/cf5f4ae933378a3b872f9b709953e431e05f308f0274f0959f8750c19eb2e0d3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d7a61666672616e2f4164617074697665436f6e666f726d616c50726564696374696f6e7354696d655365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/5a894f0122f82b702f50233d4ab3582b260e4a96af3ae8b0e4d3a9ce021775b6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4879756e576f6f6b4c2f504d2d4d656d4e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/e6e327a90ddb102aee5e480c357bb58adf748fb5f35aac3421a54d3b8ed54a76/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f73616c6573666f7263652f436f53543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICLR 2022 |
Multivariable |
Electricity Traffic M4 CASIO NP |
DEPTS |
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/ea5470d201e58d4acc10bbcd782790dbf0e0d5dc2e107de94d6676fcab74dad4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f77656966616e74742f44455054533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/b4e24778ce356381f21330e6bcbdf5934de704faf5b105a51c1e7ed409441283/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c697061792f50797261666f726d65723f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/81dd7454cd908028cb0882c8f6e12e3322c00b208d52555f7d7cbe8c36e17dd3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f74732d6b696d2f526576494e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/f81ab019c224421a45a9e356c82e0739fc57f1418ca2f63adca367637d05603e/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/ec4a37b440e5c5c77a0b2454b7330a0cd34834a8be2851f9fdfb005812310bbd/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/265fc8701c66cb4f3d62ac80237efeccf9e4ea1f92d1342a10b4d1238d9d7a22/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c69755a482d31392f4553473f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
METR-LA Solar Traffic ECG5000 |
VSF |
Multi-Variate Time Series Forecasting on Variable Subsets |
Pytorch,dgl
![Forks](https://camo.githubusercontent.com/6ba9e1681f254007e198ca225d85ff6b818ee136b49658bf2e77983c56ce448e/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/bebe5b8765489fe4f87247d3b994825e289b09bc1f1560637d7e3cff81befd07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4b4c343830352f43726f7373545265533f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/73167c63b875209e466408ca6553ad032bfa689767e9fce9796545a612e8872a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f64636c697539392f4d5344523f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
Hangzhou NYC |
DTIGNN |
Modeling Network-level Traffic Flow Transitions on Sparse Data |
Pytorch
![Forks](https://camo.githubusercontent.com/9e46c5b340ede4fec4b705b8b044cb5e255d230df8bd7dfe08bcd99e0a0374e6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736861776c656e2f647469676e6e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
KDD 2022 |
Multivariable |
Temperature Cloud cover Humidity Wind |
CLCRN |
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/1c9e048e2fb49329f3e8626e8c3e919b97646ada555fc1c6ebd40597976ba30c/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/0f76074cb908c41a0a6f088d05b687ef0a78c2348cd89cf828f1c3073c7e0376/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a656f6e677768616e63686f692f5354472d4e4344453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/b86972e7fffb18c1b59178d0f3c8d84a9be79275b08f9079839fabebb300831f/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/267904f2f2f7d10dce27ec062650fb8b9c1aaacf6a2e747fbaa4faaef8133b4f/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/571a6c45e034e5b346f8eea516dc2925803f026201c4b4e08beb5504d4b86a3c/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/b0a7a7263147137db6aa316db495c7366cba6d9b4eaa5da96634b091a540b26f/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/170012580e3f11ab3719ca0c9705629492bd423a11d3582b69d215c3db687617/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c697061792f5247534c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/dd04ea045f4bd662640d024172d9b641e1355756282cd4ee43d44f50334fd538/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67616c696231392f4465657045787472656d612d494a43414932322d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2022 |
Multivariable |
GoogleSymptoms Covid19 Power Tweet |
CAMul |
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/0d156008f8143fef5d711f98dd00a97790cfdd6b00183f6453238bd85bdcdec9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4164697479614c61622f43414d756c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2022 |
Multivariable |
Electricity Stock |
MRLF |
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction |
Pytorch
![Forks](https://camo.githubusercontent.com/d1a7e267eee045ce246a378339e0d234e5e2417ef533058e080510f0c7465bb2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f434d4c462d6769742d6465762f4d524c463f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/b6573c2cceac17b7453ed55b52644275b5e7161d4c4c834c5f9f726e7b8b4c4d/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/b0e6c5b44790ccb5cad14181f3fd9bc74f5867b1ac4a54101d4756850ebca3ff/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/ae92862bb4759f52c210e0a9fc27b2b4ed21f5cd18f285522860bdb9871b34f8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6b35312f53544753503f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/eb4b626ec67b4572412af6dd7639e86cf1e4edf3275574a242e2ce152d09bfda/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/626db7ef5ba83649ae784af99f3ec17dd2edd0e8250b4f1f40fb27feda4bd71c/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/0bf170af7dc8ec01a4bd3cf61a8544572e4cbccdb70339a16f5a31b3f61846d1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a657a68697368616f2f535449443f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/cc8266d72ff2d9609ac375f4567e239a576753c7a9636b983a05747932362a9b/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/c34e50f838a1fdf83b8cf2f73bd66106899f24195887e7e4278a02de5333e52c/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/396034365ce560bc91ebbcaea9afa041cfd6aaae1d565ca04229847fadd185f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4461696b6f6e2d53756e2f41646a7573744175746f636f7272656c6174696f6e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/84646da7a4bcd163201eda4d3039b5903b2f53fbf3bb6252a45bbfa51d748d43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f5a2d47434e4554732f5a2d47434e4554733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/edd361609ecfdacc981b902c3173529d6fef4d02e2b5464a62ffdbbc22e6ddce/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/0534052d02610a8616dfdf3675f5322121ef34875d2693256a2fde02dcb0cbae/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/93942c9ae69642290bafbacfeff25ec2d21cd8421f9a17ac4939236223e4fe0d/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/9410c69085156d283f84b8bde20b21f2a1997a73030e9ba58e908f443f057821/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6c69616e677a686568616e2f444d535447434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/66a1909db0bd0d8b535618bf73b150a1ca93361bb46058b59712a2b2622eb94a/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/671aa5bb3c4b054ed715bd04b252deee3e1148da7739d15a0d3ef780a089c8f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4a4c44656e672f53542d4e6f726d3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/6ca4b9c16b3ec828ebc7f9f893a1936e8da1e721ccca6a8f8423b3fe4ca307c2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f746875777579696e6a756e2f44474d323f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
Guangzhou Seattle HZMetro , etc. |
DSARF |
Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/b404f81ec0c683617f9e64748dd894f67136708528260f26278000dd49034a1e/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/5e390e8c4ab78f67753b368355d916890d4bbb9375d37aacbf6f08c1b75d5b58/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626f726573686b696e61692f66632d676167613f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Speed |
DiDiJiNan DiDiXiAn |
HGCN |
Hierarchical Graph Convolution Network for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/4e79c4a322ba5aaa6b102ef88056d8d912688328264ef192a0597fe356710c4c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f6b616e3938372f4847434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Multivariable |
ETT Weather Electricity |
Informer |
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/978e76cffdd00bacf45810ab8af27f05117d5a351709769b90bd4f1df5957298/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7a686f7568616f79692f496e666f726d6572323032303f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/64ba0b49cf243cf63911392a82b3849e893a3b748eb19043f71b5da0b89c5a17/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4d656e677a68616e674c492f535446474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/06c14d04b58528a049bf3bf7bd5b90e41066fe13561268be21a8a948d5ab1d62/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e696d696e676e696d696e672f67646e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2021 |
Traffic Flow |
SG-TAXI |
TrGNN |
Traffic Flow Prediction with Vehicle Trajectories |
Pytorch
![Forks](https://camo.githubusercontent.com/eb054a5b7294d1180908fb537bf506ee47bebf5aef209019e7adcca929085d85/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d696e677169616e3030302f5472474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/8cb896a05c32375ee5c479d454cf9e4397cc17d73c9ec7c039bb2cf4275e44ab/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/dc1c6edaec6a51409391283e92a9ab88bfbc8aaa41251c24e0d1f300c925f372/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f62616f79756a696e672f4e4554333f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/7d39d14d306495181f1b372c763a07920a51c30f39d7ae16dacffde7f495aab6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f616c6173646169727472616e2f726164666c6f773f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/52f48366bdfc6832d11e04b6e6d715eca00ef9fcdac55ad8f47f09cb214e04d3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f67756f73686e424a54552f415354474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/8649b7eabe3d993dc4ddea8951c9b43bec479d596edbea3bd69900b5dc0e54c4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b61736869776132302f444c2d54726166662d47726170683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/d27622ba66973ef30035551558ea2ec49b8ca7627dde72c1f5a1db6ab2bbda8a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736f6e6779616e67636f2f53544e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
ICDM 2021 |
Traffic Speed |
DiDiCD DiDiXiAn |
T-wave |
Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/d27622ba66973ef30035551558ea2ec49b8ca7627dde72c1f5a1db6ab2bbda8a/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/e9a40a26ee35ac1b1542c1cbf2268c91568c110cc0dc185362c3106a8c632b24/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f59616e674c494e313939372f5353444e65742d4943444d323032313f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/e4f465b95fd73da9fa7d6f2c9508e14e3a689ef55bea14d1778654c24b5e963d/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/37f3caa33e57437e48cb0f441aa4ba02030e9bf7ee534ace749c48dd4df37632/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c65694241492f414743524e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2020 |
Multivariable |
Electricity Traffic Wind Solar M4-Hourly |
AST |
Adversarial Sparse Transformer for Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/cc906e07a6551e8f1197d8e34bc06f900b31dcf5353ad6497ce00c0d989800d2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68696869686968697773662f4153543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/06c29b73af37376120e6246d19970aa24a633213a9440b146d62f087dfa3bf93/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d6963726f736f66742f5374656d474e4e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/bf91db1f691823ad4fdfeb2ae93d80ab7425578b3313b329ca206e339f14f506/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7068696c6970706572656d792f6e2d62656174733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/91865dbbc4bfffd692ab56d78927592e9a7a2f1e719c6f32ca5fd98f1e040e4e/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/743236ff17c4536a63750f3d1f65eea3323be01bc27c2a2c493a6b38ed29dd85/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68616f78696e676c2f4453414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/08c0ad126d739f30c66dee29882cbb65b695bcfb23277285776643f2a13e4b10/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f437572622d47414e2f437572622d47414e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/63dce63cc0a6b2ea221924b62342061f5766dfaff706433f76c69ce34bd45aa5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f536d616c6c4e616e612f53545347434e5f5079746f7263683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Multivariable |
Traffic Energy NASDAQ |
MLCNN |
Towards Better Forecasting by Fusing Near and Distant Future Visions |
Pytorch
![Forks](https://camo.githubusercontent.com/6cd4724ad7a40bbb79589942a66f3f541c68c09d7a14e6762eb0ae73afb5c3d4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f736d616c6c47756d2f4d4c434e4e2d4d756c7469766172696174652d54696d652d5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/a30bafddaf5007121d4086a085a5ef0f779964a15e4b42acf20023eb90485d25/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f626f6e616c646c692f5744475f54433f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/a22b8b8d43a7500295553f4218f44c5651ef97326a065de01626e79450f337c8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6a61636b6d69656d69652f474d454c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/6cd57751c1f7e9858aad7da78d1aeeb3f5ab1eefb93fcf59bec833ea6ea212d3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f446572726f6e58752f446565705472656e64733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/62d26139f7b3b914bead90c8460d6047ea6383df3ab62b5ccba51464f715a4a6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6875617765692d6e6f61682f4248542d4152494d413f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
AAAI 2020 |
Traffic Speed |
PEMS04 PEMS07 PEMS08 |
LSGCN |
LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks |
TF
![Forks](https://camo.githubusercontent.com/6227a62857108b379c97abbc7fa54cbb83e2076aa3a65787e51af3675d349820/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68656c616e7a68752f4c5347434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/1eb74208e258a088255e2cf3d856a3c1409a289daaa3e1c3d868c5b8099328f0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f7061626c6f766963656e74652f6b657261732d776174746e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/af666be67b444233399714a374d5baae73e89a367496e6014b0f3e5270891b8c/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/b08c361ddb013060631a561777d20eb2ad10e4c875966843055cf14a63683712/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f526f62696e4c75313230392f535441472d47434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/792920dfd9a78f8b4fcb61cc2cba1e644bfd029acfc40dcd1965f3299421bb64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f4c4d6973736865722f53542d475241543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/341bdbd5bd6745324448cad317d3c38004dfebe38bd92eb189fbbe2665c74f4d/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/8c6473a39306de4abe280708166bac04086dfbd4abaf34239ac937cf8c2b92ca/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/0fb7c35b96f10cb0ccc48878933922e626b6abc9d26f290a8704a3fea88d106f/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/c4b64fc3eb6a98b09f1d634192e640faabaa83d913d4a52f778d111f4813cb3b/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/a36790f75686575996df18f32401898fbc47516336e1f940484b4998761f9ab0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f79696e677875652d7a68616e672f6353542d4d4c3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/faa414f6216ad19907f54ee0384dbdd7cecd0792251237ecddafe102d6002f1e/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/17688de04e5045059a59dc105297aed68ce74a5c4ffb094e10f3df783b4c8906/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6d6c706f747465722f5472616e73666f726d65725f54696d655f5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/9f5bf479c7b85a0b23ba536df6bbc50519f19b561c1f81f2ed061e9fb7c55d62/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f76696e63656e742d6c656775656e2f44494c4154453f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
NeurIPS 2019 |
Traffic Flow |
Earthquake |
DeepUrbanEvent |
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events |
Keras
![Forks](https://camo.githubusercontent.com/145ca7c1ce6f81fc45ea6cb3dd58504b7ecd68618ab3eb35c9b4570d5843b592/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f646565706b6173686977612f44656570557262616e4576656e743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/dc7b39e6b052f23e54d582617ac377226813be5376aafa4f9d30c0dfcfad06a8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f70616e7a686579692f53542d4d6574614e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/1d38d96a5a37f6481c6037f3593ec2e7040c86095e496f611b2aa36572037d9e/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/796e4ac1bea1aa0972ccc8003216aa53e27245f8af7b4c7948d2131bf6401c80/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f446176696468616d332f41535447434e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/c87f21d2448efdeb33a4686cbc34239d6fbf71a4579e876d483ba1c5a73ef658/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f74616e677869616e66656e672f5354444e3f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/885c52fcca17803d4156930b5d08c391b71b7235441e4e4ce84a2ea54cefc2db/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/f6f228c7bac89a702b380547277dab231472703f1a532b5b538fa66a6855315a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f576f6f6453756761722f4753544e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2019 |
Traffic Speed |
METR-LA PEMS-BAY |
GWN |
Graph WaveNet for Deep Spatial-Temporal Graph Modeling |
Pytorch
![Forks](https://camo.githubusercontent.com/e35b1373835c4c5b6c1b4faa00d4635a73a8becfd50d74e3895b9115bd49c610/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6e7a68616e2f47726170682d576176654e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/7937857399ea9d0c1772505a0ea1d2697ecf2e5ebcba964a6e61585eca785c2d/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/069f015a9cbd4825bef5e59392a1dd697eb18941de9f7743a19ba668ede50437/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6d7572616c6964312f44796e616d6963417474656e74696f6e4e6574776f726b733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/04d9d53a9c27527c7a64196e729e211ee92d630c8afd86d9da1cb9e52418e7ac/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f68756178697579616f2f4d65746153543f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
WWW 2019 |
TrafficPred imputation |
GZSpeed HZMetro Seattle London |
BTF |
Bayesian Temporal Factorization for Multidimensional Time Series Prediction |
Python
![Forks](https://camo.githubusercontent.com/069f015a9cbd4825bef5e59392a1dd697eb18941de9f7743a19ba668ede50437/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6e6d7572616c6964312f44796e616d6963417474656e74696f6e4e6574776f726b733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
TPAMI 2019 |
Multivariable |
Gas Station |
DSANet |
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting |
Pytorch
![Forks](https://camo.githubusercontent.com/0badf11c98870f1825de4fbf3ae21a4ad6c2823d1b518827ff911392163c0985/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f6269676875616e673632342f4453414e65743f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/600fc73e21015375215abf7a3bf83ff14903856a46198692392daf843a52ce48/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f647579616e687068616d2d6272732f5841492d4d756c7469766172696174652d54696d652d5365726965733f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
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/46d662afc9bc4367df2b899a427280d7b3b384b662fd785a6f61a87dd6ee88ef/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f666f726b732f416775696e2f535447434e2d5079546f7263683f636f6c6f723d637269746963616c267374796c653d736f6369616c) |
IJCAI 2018 |
Traffic Speed |
METR-LA PEMS-BAY |
DCRNN |
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting |
TF
Pytorch |
ICLR 2018 |