Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
Kiran Madhusudhanan Shayan Jawed Lars Schmidt-Thieme
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.