GestaltCogTeam/BasicTS

Health v2 · maintenance only

Adoption not part of the score

1814 stars · 216 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BasicTS is a Python benchmark library and toolkit for fair and scalable time series analysis, built on PyTorch. It supports forecasting, classification, and imputation tasks with statistical, machine learning, and deep learning baselines.

Use cases

• benchmark time series forecasting models fairly
• train traffic flow forecasting models
• run long-term time series forecasting experiments
• compare deep learning baselines for time series
• impute missing values in time series data
• build custom time series models with modular components

When to choose

• you need reproducible, fair comparisons across time series forecasting models
• you want a PyTorch toolkit with plug-and-play components for forecasting, classification, or imputation
• you work on spatial-temporal or long-term forecasting research

When to avoid

• you need production time series forecasting as a service rather than a research toolkit
• you work outside Python/PyTorch ecosystems
• you need non-time-series ML tasks

Facets

library · maturity active

benchmarking machine-learning deep-learning data-science time-series machine-learning data-science python time-series-forecasting traffic-forecasting pytorch benchmark spatial-temporal-forecasting long-term-forecasting imputation classification gpu

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markdown · JSON · MCP: product_card(name="GestaltCogTeam/BasicTS")