一种用于时间序列预测的平移不变神经网络结构
A Translation-Invariant Neural Network Architecture for Time Series Forecasting
张晗1
作者信息
- 1. 东北财经大学数据科学与人工智能学院,大连 116025
- 折叠
摘要
全序列时序形态相似性度量方法通常存在不能从整体上挖掘时序间形态趋势变化的问题.对此,提出了一种具有丰富输出表示的扩展层,并结合自编码器网络自动从时序数据中学习具有平移不变的全局相似性,实现对时序数据的全局特征提取和时间序列预测效果的提升.实验结果表明,所提模型在多个现实世界时序数据集的预测任务中的所有情况下都显示出优异的性能.
Abstract
Targeting the problem that the whole series morphological similarity measurement method usually cannot mine the morphological trend change between time series as a whole,an extended layer with rich output representation is proposed,and the auto-encoder network is combined to automatically learn the global similarity with translation invariant from time series data,to realize the global feature extraction of time series data and the improvement of time series prediction effect.Experimental results show that the proposed structure performs excellently in all cases in the forecasting task of multiple real-world time series datasets.
关键词
时间序列/平移不变性/神经网络/自编码器/扩展层Key words
time series/translation invariance/neural networks/auto-encoders/extended layer引用本文复制引用
基金项目
辽宁省应用基础研究计划项目(2023JH2/101600040)
辽宁省教育厅基本科研项目(LJKMZ20221598)
国家自然科学基金项目(72273019)
出版年
2024
NETL
NSTL
万方数据