TSPP: A Unified Benchmarking Tool for Time-series Forecasting
This addresses the problem of inconsistent comparisons for practitioners and researchers in time series forecasting, though it is incremental as it focuses on benchmarking rather than new methods.
The authors tackled the lack of standardization in time series forecasting by proposing a unified benchmarking framework, and they demonstrated that deep learning models with minimal effort can rival gradient-boosting decision trees that require extensive feature engineering.
While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment of machine learning solutions in real-world settings necessitates standardization throughout the entire pipeline. This need is particularly acute in time series forecasting, where diverse settings impede meaningful comparisons between various methods. To bridge this gap, we propose a unified benchmarking framework that exposes the crucial modelling and machine learning decisions involved in developing time series forecasting models. This framework fosters seamless integration of models and datasets, aiding both practitioners and researchers in their development efforts. We benchmark recently proposed models within this framework, demonstrating that carefully implemented deep learning models with minimal effort can rival gradient-boosting decision trees requiring extensive feature engineering and expert knowledge.