TimeGym: Debugging for Time Series Modeling in Python
This toolkit addresses debugging inefficiencies for time series forecasters, though it appears incremental as it builds on existing testing methodologies.
The authors tackled the challenge of debugging time series forecasting pipelines by introducing TimeGym, a Python toolkit that simplifies testing through generic tests based on common modeling issues, enabling a Test-Driven Development approach with artificial data generation.
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.