decisionintelligence/TFB
[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
This project helps data scientists, machine learning engineers, and researchers rigorously compare the performance of various time series forecasting models. You provide your time series datasets and the project generates benchmark results, allowing you to understand which forecasting methods are most suitable for different data characteristics. This is for anyone building or evaluating predictive models for future trends.
1,669 stars.
Use this if you need to fairly and comprehensively evaluate multiple time series forecasting methods against each other using a standardized framework.
Not ideal if you are looking for a simple, out-of-the-box forecasting solution without needing to compare various models.
Stars
1,669
Forks
115
Language
Shell
License
MIT
Category
Last pushed
Jan 15, 2026
Commits (30d)
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