计算机工程与应用2025，Vol.61Issue(22)：170-182,13.DOI:10.3778/j.issn.1002-8331.2408-0176
基于深度学习和时序拆解的长期时序预测模型
Long-Term Time Series Prediction Model Based on Deep Learning and Temporal Decomposition
摘要
Abstract
In recent years,some research efforts have introduced Transformer and its variants into general time series pre-diction tasks,achieving significant performance improvements.However,such models currently face several challenges in long-term time series prediction tasks,including high computational costs,limited ability to explore long-term relation-ships within the data,and restricted receptive fields.To address these issues,a new long-term time series prediction model named DeepTD-LSP(deep temporal decomposition long-term series prediction)is proposed based on the ideas of attention mechanism and temporal decomposition.The model adopts an encoder-decoder structure overall.The encoder consists of multiple stacked encoding layers,with each layer composed of frequency decomposition modules,feedforward modules,and seasonal modules.The frequency domain decomposition module separates the input sequence into trend information,seasonal information,and noise.Among these,seasonal information is the most complex and challenging to extract,hence in this model,the encoder primarily focuses on mining and encoding seasonal information.The decoder consists of multiple stacked decoding layers,with each layer composed of frequency decomposition modules,feedforward modules,seasonal modules,seasonal attention modules,and trend attention modules.The decoder is mainly responsible for extracting,inte-grating,and predicting various types of input information.The model is tested on seven real-world datasets,the experi-mental results demonstrate the superiority of the DeepTD-LSP model over other long-term time series prediction models.关键词
时序预测/深度学习/时序分解/注意力机制Key words
time series prediction/deep learning/time series decomposition/attention mechanism分类
信息技术与安全科学引用本文复制引用
宋晓宝,邓力玮,王浩,张耀安,陈作胜,贺钰昕,曹文明..基于深度学习和时序拆解的长期时序预测模型[J].计算机工程与应用,2025,61(22):170-182,13.基金项目
国家自然科学基金青年科学基金(62206178) （62206178）
深圳市高等院校稳定支持计划(20231121221536001). （20231121221536001）
