Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

arXiv:2403.04477v1 Announce Type: new
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 …

abstract arxiv cs.lg deep learning events forecasting future hyperparameter impact patterns research series time series time series forecasting trends type