Building fuzzy random autoregression model and its application

Lu Shao; You Hsi Tsai; Junzo Watada; Shuming Wang

doi:10.1007/978-3-642-29977-3_16

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 proceeding › Chapter

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Smart Innovation, Systems and Technologies
Pages	155-164
Number of pages	10
Volume	15
DOIs	https://doi.org/10.1007/978-3-642-29977-3_16
Publication status	Published - 2012

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	15
ISSN (Print)	21903018
ISSN (Electronic)	21903026

Keywords

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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10.1007/978-3-642-29977-3_16

Cite this

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title = "Building fuzzy random autoregression model and its application",

abstract = "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.",

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T1 - Building fuzzy random autoregression model and its application

AU - Shao, Lu

AU - Tsai, You Hsi

AU - Watada, Junzo

AU - Wang, Shuming

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Confidence interval

KW - Fuzzy autoregression model

KW - Fuzzy random autoregression model

KW - Fuzzy random data

KW - Time series data

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M3 - Chapter

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VL - 15

T3 - Smart Innovation, Systems and Technologies

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BT - Smart Innovation, Systems and Technologies

ER -

Building fuzzy random autoregression model and its application

Abstract

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Keywords

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