TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting  

作　　者：Haoran Huang Weiting Chen Zheming Fan 

机构地区：[1]MOE Research Center of Software/Hardware Co-Design Engineering,East China Normal University,Shanghai,200062,China

出　　处：《Computers, Materials & Continua》2024年第3期3665-3681,共17页计算机、材料和连续体（英文）

基　　金：supported by the National Key Research and Development Program of China(No.2018YFB2101300);the National Natural Science Foundation of China(Grant No.61871186);the Dean’s Fund of Engineering Research Center of Software/Hardware Co-Design Technology and Application,Ministry of Education(East China Normal University).

摘　　要：Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

关 键 词：DIFFERENCE data prediction time series temporal convolutional network dilated convolution 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]