decisionintelligence/TFB

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1734 stars · 123 forks observed · 2026-08-28

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

TFB is a comprehensive and fair benchmarking framework for time series forecasting methods, covering deep learning, machine learning, and statistical models. It provides unified datasets, pipelines, and evaluation protocols, published as a PVLDB 2024 paper.

Use cases

• benchmark time series forecasting models fairly
• compare deep learning vs statistical forecasting methods
• evaluate multivariate time series forecasting on standard datasets
• compute time series characteristics like trend and seasonality
• reproduce forecasting experiment results with unified hyperparameters

When to choose

• you need a standardized, fair comparison of forecasting algorithms
• you want ready-to-use pipelines across 27+ multivariate time series datasets
• you are doing academic research on time series forecasting

When to avoid

• you need production forecasting deployment rather than benchmarking
• your task is time series classification or anomaly detection instead of forecasting

Facets

framework · maturity active

benchmarking machine-learning deep-learning data-science time-series machine-learning data-science python cross-platform time-series-forecasting benchmark pytorch evaluation pvldb research linux

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