TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness

TL;DR

TimeRecipe introduces a comprehensive benchmarking framework that evaluates individual components of time-series forecasting models, providing insights and recommendations to improve model design and performance across diverse scenarios.

Contribution

It systematically assesses module-level effectiveness in time-series forecasting, revealing design insights and outperforming existing methods through extensive experiments.

Findings

Exhaustive exploration improves forecasting accuracy.

Specific design choices are linked to different forecasting scenarios.

Toolkit recommends suitable architectures based on empirical insights.

Abstract

Time-series forecasting is an essential task with wide real-world applications across domains. While recent advances in deep learning have enabled time-series forecasting models with accurate predictions, there remains considerable debate over which architectures and design components, such as series decomposition or normalization, are most effective under varying conditions. Existing benchmarks primarily evaluate models at a high level, offering limited insight into why certain designs work better. To mitigate this gap, we propose TimeRecipe, a unified benchmarking framework that systematically evaluates time-series forecasting methods at the module level. TimeRecipe conducts over 10,000 experiments to assess the effectiveness of individual components across a diverse range of datasets, forecasting horizons, and task settings. Our results reveal that exhaustive exploration of the design space can yield models that outperform existing state-of-the-art methods and uncover meaningful intuitions linking specific design choices to forecasting scenarios. Furthermore, we release a practical toolkit within TimeRecipe that recommends suitable model architectures based on these empirical insights. The benchmark is available at: https://github.com/AdityaLab/TimeRecipe.