A game theoretic approach to explain the output of any machine learning model.

Scalable and user friendly neural 🧠 forecasting algorithms.

Popol Vuh: Nixtla's operating system

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

Canonical Autocorrelation Embeddings