Stars
A game theoretic approach to explain the output of any machine learning model.
Scalable and user friendly neural 🧠 forecasting algorithms.
Scalable machine 🤖 learning for time series forecasting.
Lightning ⚡️ fast forecasting with statistical and econometric models.
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
Overview of State-of-the-art Deep Learning Based Methods for Time Series Forecasting
Datasets for time series forecasting
Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.
Transfer 🤗 Learning for Time Series Forecasting
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.
Probabilistic time series modeling in Python
KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's …
An open-access benchmark and toolbox for electricity price forecasting