Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

TL;DR

This paper investigates how hyperparameters like context length and validation strategy affect MLP performance in probabilistic time series forecasting, supported by extensive experiments and a large new metadataset.

Contribution

It introduces TSBench, the largest metadataset for time series forecasting, and demonstrates its utility in hyperparameter optimization.

Findings

Hyperparameter tuning significantly impacts forecasting accuracy.

The new metadataset enables more effective hyperparameter optimization.

Extensive experiments reveal key hyperparameters for MLP performance.

Abstract

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context length and validation strategy, on the performance of the state-of-the-art MLP model in time series forecasting. We have conducted a comprehensive series of experiments involving 4800 configurations per dataset across 20 time series forecasting datasets, and our findings demonstrate the importance of tuning these parameters. Furthermore, in this work, we introduce the largest metadataset for timeseries forecasting to date, named TSBench, comprising 97200 evaluations, which is a twentyfold increase compared to previous works in the field. Finally, we demonstrate the utility of the created metadataset on multi-fidelity hyperparameter optimization tasks.