Forecasting using Fuzzy Time Series

Chellai, Fatih (2022): Forecasting using Fuzzy Time Series.

Abstract

This chapter is a very short introduction to Fuzzy Time Series (FTS) models. The aim is to present an overview of the concepts of fuzzy logic, fuzzy set theory, and fuzzy time series framework. Accordingly, the chapter has a full application dimension of the FTS models as a main vocation. The R program was used to fit and forecast the principal FTS models, where real datasets of road traffic accidents in Algeria have been used. This chapter is organized as follows; the first section presents the concept of fuzzy logic, the second section is devoted to the Fuzzy Time Series, where we define a fuzzy set and universe of discourse. The third section summarizes the main models of fuzzy time series, precisely; we presented the (Song & Chissom, 1993) model, the (Chen, 1996) model, the Heuristic (Huarng, 2001) model, the (Abbasov & Mamedova, 2003) model, the (Chen & Hsu, 2004) model, and the (Singh, 2008) model. The fourth section is a case application of these models on the number of accidents in Algeria; the “AnalyzeTS” package of the R program was used to demonstrate the steps of estimation and forecasting.

• Item Type: MPRA Paper
• Language: English
• Keywords: Fuzzy logic; Forecasting; Time Series
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software

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