A Systematic Review for Transformer-based Long-term Series Forecasting

TL;DR

This paper provides a comprehensive review of transformer architectures for long-term time series forecasting, covering datasets, evaluation metrics, training techniques, and future research directions.

Contribution

It offers a systematic overview of transformer-based methods for LTSF, highlighting recent advancements, best practices, and open challenges in the field.

Findings

Transformers are highly effective for long-term time series forecasting.

Various transformer variants improve accuracy and efficiency in LTSF tasks.

The paper identifies key datasets and evaluation metrics used in LTSF research.

Abstract

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field.