Deep Learning Methods for Time Series Forecasting: A Comparative Review

Abstract

Time series forecasting involves analyzing the trends and patterns of variables over time to predict future values, which is crucial for decision-making in diverse fields such as finance, meteorology, agriculture, industry, and medicine. With the rapid advancement of sensor and network technologies, vast amounts of time series data have been generated, emphasizing the need for accurate forecasting methods. This paper provides a comprehensive review of traditional and machine learning approaches for time series prediction, with a focus on deep learning methods.  The review highlights the strengths and limitations of these deep learning models, providing insights into their practical applications. Additionally, we discuss the future development of deep learning methods in this domain, suggesting directions for enhancing prediction accuracy and model efficiency.