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Tsai

Tsai is an open-source Python deep-learning library built on PyTorch and fastai for time-series and sequence tasks, including classification, regression, forecasting and imputation.

Independent OpenViST project profile, based on linked sources.

Tsai — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Tsai when you want a Python library for developing deep-learning workflows on sequential data and want access to its listed model families, task support and time-series evaluation utilities.

Who it is for

  • Python developers working with time-series or sequential datasets.
  • Practitioners building deep-learning experiments for classification, forecasting or regression.
  • Researchers comparing recurrent, convolutional and transformer model approaches to time-series tasks.

Documented uses

  • Develop deep-learning workflows for time-series classification.
  • Build forecasting or regression experiments on sequential data.
  • Explore listed CNN, RNN and transformer architectures for time-series problems.
  • Use pipeline transforms and walk-forward cross-validation in time-series workflows.
Before you choose

Requirements & limits

The project is open source under Apache-2.0. Its README specifies Python 3.10 or newer and recommends PyPI for new installations; the current Conda channel is no longer updated.

  • Python 3.10 or newer is required.
  • PyTorch and fastai are the library's documented foundations.
  • Only hard dependencies are installed by default; some selected tasks use optional packages.
  • The project documents models including LSTM, GRU, FCN, InceptionTime, TST and PatchTST.
  • The README describes downloadable datasets across classification, regression and forecasting.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Tsai from PyPI

Set up the library in a Python environment using the project's documented stable-install method.

Before you start

  • Python 3.10 or newer
  • pip

Steps

  1. Install the latest stable release with the command below.

    Documented command
    pip install tsai
  2. Install task-specific optional packages if prompted or needed; the project names sktime, tsfresh, PyWavelets and nbformat as optional dependencies.

Expected outcome

Tsai is installed from PyPI, with optional dependencies available for selected tasks.

Guide limitations

  • The guide covers installation only, not model training or execution.
  • The current Conda channel is no longer updated; older releases remain available there.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
timeseriesAI/tsai · Official homepage title · retained in its source language