kailu22's Stars
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
facebookresearch/Kats
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
yzhao062/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
flennerhag/mlens
ML-Ensemble – high performance ensemble learning
KindXiaoming/pykan
Kolmogorov Arnold Networks
alicezheng/feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
tblume1992/MFLES
osllogon/epf-transformers
Official implementation of "A Transformer approach for Electricity Price Forecasting"
mlflow/mlflow
Open source platform for the machine learning lifecycle
kwuking/TimeMixer
[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"
elephaint/pgbm
Probabilistic Gradient Boosting Machines
WenjieDu/PyPOTS
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
lss-1138/SegRNN
The official repository of the SegRNN paper: "Segment Recurrent Neural Network for Long-Term Time Series Forecasting." This work is developed by the Lab of Professor Weiwei Lin (linww@scut.edu.cn), South China University of Technology; Pengcheng Laboratory.
Y-debug-sys/Diffusion-TS
[ICLR 2024] Official Implementation of "Diffusion-TS: Interpretable Diffusion for General Time Series Generation"
decisionintelligence/TFB
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
jiwidi/time-series-forecasting-with-python
A use-case focused tutorial for time series forecasting with python
howard-hou/RWKV-TS
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
DaoSword/Time-Series-Forecasting-and-Deep-Learning
Resources about time series forecasting and deep learning.
amazon-science/chronos-forecasting
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
sktime/pytorch-forecasting
Time series forecasting with PyTorch
junwoopark92/Self-Supervised-Contrastive-Forecsating
"Self-Supervised Contrastive Learning for Long-term Forecasting", accepted at International Conference on Learning Representations (ICLR) 2024
sivahemang2002/Load-and-Solar-Forecasting
MicheleUIT/Probabilistic-load-forecasting-with-Reservoir-Computing
Probabilistic load forecasting with Reservoir Computing
Water2sea/WITRAN
zshhans/MSD-Mixer
[VLDB 2024] A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
YangyangFu/transformer-time-series
A library to benchmark various transformer and non-transformer models on time series forcasting, imputation and abnormality detection
DAMO-DI-ML/NeurIPS2023-One-Fits-All
The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"
icantnamemyself/SAN
Pytorch implementation of NIPS'23 paper: Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
aikunyi/FreTS
Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting"
thuml/Koopa
Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803