timeseriesAI/tsai

Health v2 · maintenance only

Adoption not part of the score

6111 stars · 721 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

tsai is an open-source deep learning library built on PyTorch and fastai for time series and sequential data tasks such as classification, regression, forecasting, and imputation. It provides state-of-the-art models (PatchTST, InceptionTime, ROCKET, RNNs with attention), built-in benchmark datasets, sklearn-style pipeline transforms, and walk-forward cross-validation.

Use cases

• classify time series with deep learning in pytorch
• forecast time series with PatchTST or transformer models
• train a model for time series regression
• benchmark time series classification models like InceptionTime and ROCKET
• impute missing values in time series data
• do walk-forward cross-validation for time series forecasting
• download UCR/UEA time series datasets easily

When to choose

• you want state-of-the-art deep learning models for time series in Python
• you already use PyTorch or fastai and need time series support
• you need quick access to standard time series benchmark datasets
• you want sklearn-like pipelines and walk-forward validation for forecasting

When to avoid

• you need classical statistical forecasting methods like ARIMA or Prophet
• you work outside Python or without GPU support
• you need a lightweight tabular ML tool rather than deep learning
• you rely on conda distribution, which is no longer updated

Facets

library · maturity active

deep-learning machine-learning data-science time-series deep-learning machine-learning python pytorch fastai time-series-classification time-series-regression forecasting imputation transformer patchtst inceptiontime rocket self-supervised gpu

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