TSFLab workbench for TSF research
Original title: TSFLab: A Self-Improving Agent-Agnostic Workbench for Time Series Forecasting Research
TSFLab uses AI agents to improve time series forecasting research with lower costs and better accuracy.
TSFLab provides a self-improving workbench for time series forecasting papers often lacking code. It reduces tokens used in reproduction and benchmarking by 11-66%. Pilot shows faster method development.
- 20-163% task completion improvement
- 11-66% fewer tokens per task
- 32.8-73.6% less development time
From the original article
1 min read at huggingface.co
As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden of analyzing existing work. First, not all publications release code. For instance, only about 20% of arXiv TSF papers have a code link in the surveyed period.
By ChengAo Shen, Yanjun Zhao, Haoxin Liu · huggingface.co
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