Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data

Part of the Intelligent Systems Reference Library book series (ISRL, volume 47)

Abstract

The objective of economic analysis is to interpret the past, present or future economic state by analyzing economic data. Economic analyses are typically based on the time-series data or the cross-section data. Time-series analysis plays a pivotal role in analyzing time-series data. Nevertheless, economic systems are complex ones because they involve human behaviors and are affected by many factors. When a system includes substantial uncertainty, such as those concerning human behaviors, it is advantageous to employ a fuzzy system approach to such analysis. In this paper, we compare two fuzzy time-series models, namely a fuzzy autoregressive model proposed by Ozawa et al. and a fuzzy autocorrelation model proposed by Yabuuchi andWatada. Both models are built based on the concepts of fuzzy systems. In an analysis of the Nikkei Stock Average, we compare the effectiveness of the two models. Finally, we analyze tick-by-tick data of stock dealing by applying fuzzy autocorrelation model.

Keywords

fuzzy AR model fuzzy autocorrelation possibility economic analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of EconomicsShimonoseki City UniversityShimonosekiJapan
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan

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