A Translation-Invariant Neural Network Architecture for Time Series Forecasting

被引：0
|
作者：
Zhang, Han [1 ]
机构：
[1] School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2024年 / 47卷 / 03期
关键词：
auto-encoders; extended layer; neural networks; time series; translation invariance;
D O I：
10.13190/j.jbupt.2023-132
中图分类号：
学科分类号：
摘要：
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. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.
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页码：103 / 110
页数：7
相关论文
共 24 条
  • [1] FAKHRAZARI A, VAKILZADIAN H., A survey on time series data mining[C], IEEE International Conference on Electro Information Technology, pp. 476-481, (2017)
  • [2] LANGKVIST M, KARLSSON L, LOUTFI A., A review of unsupervised feature learning and deep learning for timeseries modeling, Pattern Recognition Letters, 42, pp. H-24, (2014)
  • [3] KRAWCZYK B., Learning from imbalanced data
  • [4] open challenges and future directions, Progress in Artificial Intelligence, 5, 4, pp. 221-232, (2016)
  • [5] GAMA J, ZLIOBAITE I, BIFET A, Et al., A survey on concept drift adaptation, ACM Computing Surveys (CSUR), 46, 4, pp. 1-37, (2014)
  • [6] XIANG Z L, WANG R, YU X R, Et al., Experimental analysis of similarity measurements for multivariate time series and its application to the stock market, Applied Intelligence, 53, 21, pp. 1-17, (2023)
  • [7] RAKTHANMANON T, KEOGH E., Fast shapelets: a scalable algorithm for discovering time series shapelets, Proceedings of the 2013 SIAM International Conference on Data Mining (SDM), pp. 668-676, (2013)
  • [8] BERNDT D J, CLIFFORD J., Using dynamic time warping to find patterns in time series [C], Proceedings of the KDD Workshop, pp. 359-370, (1994)
  • [9] LAI J H, YUEN P C, FENG G C., Face recognition using holistic fourier invariant features, Pattern Recognition, 34, 1, pp. 95-109, (2001)
  • [10] TOMASZ Arodz, Invariant object recognition using radon-based transform, Computing and Informatics, 24, 2, pp. 183-199, (2005)
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