Building fuzzy random autoregression model and its application

Lu Shao*, You Hsi Tsai, Junzo Watada, Shuming Wang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter

    3 Citations (Scopus)


    The purpose of economic analysis is to interpret the history, present and future economic situation based on analyzing economical time series data. The autoregression model is widely used in economic analysis to predict an output of an index based on the previous outputs. However, in real-world economic analysis, given the co-existence of stochastic and fuzzy uncertainty, it is better to employ a fuzzy system approach to the analysis. To address regression problems with such hybridly uncertain data, fuzzy random data are introduced to build the autoregression model. In this paper, a fuzzy random autoregression model is introduced and to solve the problem, we resort to some heuristic solution based on σ -confidence intervals. Finally, a numerical example of Shanghai Composite Index is provided.

    Original languageEnglish
    Title of host publicationSmart Innovation, Systems and Technologies
    Number of pages10
    Publication statusPublished - 2012

    Publication series

    NameSmart Innovation, Systems and Technologies
    ISSN (Print)21903018
    ISSN (Electronic)21903026


    • Confidence interval
    • Fuzzy autoregression model
    • Fuzzy random autoregression model
    • Fuzzy random data
    • Time series data

    ASJC Scopus subject areas

    • Computer Science(all)
    • Decision Sciences(all)


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