Time series prediction model for sequential learning

Manabu Gouko, Yoshihiro Sugaya, Hirotomo Aso

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

As a time series prediction model for sequential learning considering memory size limitations, this paper proposes the Adaptive and Sequential Learning Network (ASLN) model. The proposed model sequentially memorizes time sequence information given as the input and then performs prediction based on the memory. While effective use of the memory capacity is attempted for changes in the ambient environment, the model can follow up by varying its own memory. The model memorizes the elements contained in the input time series and the information on their transitions. It identifies the elements with a higher frequency of inputs among the time series and memorizes them as priority items. Information on the transitions of the input elements is represented as a state vector and is memorized by a layered neural network. The state vector maintains information on the past input sequence so that expression of the context is possible. A numerical experiment shows that the proposed model can predict a time series while tracking environmental changes. An experiment on learning of a number sequence was performed, using handwritten number patterns containing fluctuations. The prediction capability was verified with an increasing number of patterns. Guidelines are also provided for setting of parameters, which is important when the model memorizes the transition information of the time series.

Original languageEnglish
Pages (from-to)129-139
Number of pages11
JournalElectronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
Volume90
Issue number12
DOIs
Publication statusPublished - 2007 Dec 1

Keywords

  • Memory capacity
  • Neural network
  • Sequential learning
  • Time series prediction

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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