A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
This provides guidance for industry practitioners and academic researchers in efficiently optimizing hyperparameters for time series forecasting, but it is incremental as it builds on existing models without introducing new methods.
The authors tackled the lack of a unified hyperparameter optimization pipeline for transformer-based time series forecasting models by presenting one and conducting experiments on standard benchmarks, showing it is generalizable to other models like Mamba and TimeMixer.
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require extensive hyperparameter optimization (HPO) to achieve the best possible performance, and a unified pipeline for HPO in transformer-based TSF remains lacking. In this paper, we present one such pipeline and conduct extensive experiments on several state-of-the-art (SOTA) transformer-based TSF models. These experiments are conducted on standard benchmark datasets to evaluate and compare the performance of different models, generating practical insights and examples. Our pipeline is generalizable beyond transformer-based architectures and can be applied to other SOTA models, such as Mamba and TimeMixer, as demonstrated in our experiments. The goal of this work is to provide valuable guidance to both industry practitioners and academic researchers in efficiently identifying optimal hyperparameters suited to their specific domain applications. The code and complete experimental results are available on GitHub.