1005-2615 2025 57 5 799 821 10.16356/j.1005-2615.2025.05.002 article Review of Time Series Forecasting Methods Based on Deep Learning 深度学习因能够更好地捕捉时间序列数据中的复杂关系和模式而成为解决时间序列预测的有效方法。典型的做法是单独地学习这些任务，为每个任务训练1个单独的神经网络，在时间序列预测中取得了丰硕的成果。最近的多任务学习技术通过学习共享知识联合处理多个预测任务，在性能、计算和内存占用方面显示出了其优势。本文首先综述了以卷积神经网络、循环神经网络、注意力机制、Transformer和图神经网络为代表的时间序列预测深度模型，包括数据集、模型特点和性能；然后深入分析了深度多任务时间序列预测模型，按照参数共享方式和参数共享（交互）位置进行分类概述，并讨论了一些常见的多任务时间序列预测框架。最后对深度时间序列预测面临的问题和挑战进行了总结，并对未来研究趋势进行了展望。 Deep learning has emerged as an effective solution for time series forecasting due to its superior ability to capture complex relationships and patterns within temporal data. A typical approach involves learning these tasks individually and training a separate neural network for each task， which has yielded fruitful results in time series forecasting. Recent advances in multitask learning techniques have demonstrated their advantages in terms of performance， computation， and memory usage by jointly processing multiple prediction tasks through learning shared knowledge. This paper presents the first comprehensive review of methods for multitask time series forecasting. It begins by summarizing deep models for time series forecasting， represented by convolutional neural networks， recurrent neural networks， attention mechanisms， Transformer and graph neural networks， including datasets， model characteristics， and performance. Subsequently， an in-depth analysis of deep multitask time series forecasting models is conducted， categorizing them based on parameter sharing methods and the location of parameter sharing （or interaction）， and discussing some common multitask time series forecasting frameworks. Finally， this paper summarizes the challenges faced by deep time series forecasting and offers insights into future research trends. 深度学习;时间序列预测;多任务时间序列预测;参数共享;参数交互 deep learning;time series forecasting;multi-task time series forecasting;parameter sharing;parameter interaction 潘志松,韩笑,黎维 PAN Zhisong,HAN Xiao,LI Wei 1.陆军工程大学指挥控制工程学院,南京 210007;2.陆军装甲兵学院信息通信系,北京 100072 1.College of Command and Control Engineering， Army Engineering University of PLA， Nanjing 210007， China;2.Department of Information Communication， Army Academy of Armored Forces， Beijing 100072， China https://jnuaa.nuaa.edu.cn/njhkht/article/abstract/202505002