Deep learning-based time series forecasting

Abstract

With the advancement of deep learning algorithms and the growing availability of computational power, deep learning-based forecasting methods have gained significant importance in the domain of time series forecasting. In the past decade, there has been a rapid rise in time series forecasting approaches. This paper comprehensively reviews the advancements in deep learning-based forecasting models spanning 2014 to 2024. We provide a comprehensive examination of the capabilities of these models in capturing correlations among time steps and time series variables. Additionally, we explore methods to enhance the efficiency of long-term time series forecasting and summarize the diverse loss functions employed in these models. Moreover, this study systematically evaluates the effectiveness of these approaches in both univariate and multivariate time series forecasting tasks across diverse domains. We comprehensively discuss the strengths and limitations of various algorithms from multiple perspectives, analyze their capacity to capture different types of time series information, including trend and season patterns, and compare methods for enhancing the computational efficiency of these models. Finally, we summarize the experimental results and discuss the future directions in time series forecasting. Codes and datasets are available at https://github.com/TCCofWANG/Deep-Learning-based-Time-Series-Forecasting.