TimeGym: Debugging for Time Series Modeling in Python
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Abstract (arXiv)
We introduce the TimeGym Forecasting Debugging Toolkit, a Python library for testing and debugging time series forecasting pipelines. TimeGym simplifies the testing forecasting pipeline by providing generic tests for forecasting pipelines fresh out of the box. These tests are based on common modeling challenges of time series. Our library enables forecasters to apply a Test-Driven Development approach to forecast modeling, using specified oracles to generate artificial data with noise.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 4 May 2021.
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References · 5
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 218
- 2019Taxonomy of Real Faults in Deep Learning Systemscited by 90
- 2018On Testing Machine Learning Programscited by 48
- 2018METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systemscited by 8
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