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Trends and Perspectives in Linear Statistical Inference pp 69–87Cite as

Bootstrap Type-1 Fuzzy Functions Approach for Time Series Forecasting

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Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

In this study, we proposed an alternative approach for time series forecasting. Many approaches have been developed and applied for forecasting in the literature. In the past years, most of these approaches are fuzzy system modelling approaches. Fuzzy functions approaches were proposed by Turksen (Appl Soft Comput 8:1178–1188 2008) because traditional fuzzy system modelling approaches are generally based on the fuzzy rule base. Fuzzy functions approaches do not need to use the rule base. Fuzzy functions approaches should employ randomness, and their values change randomly from sample to sample. Taking into consideration this change, researchers need to obtain estimators, but this process for nonlinear models is not an easy task to carry out. Thus, bootstrap methods can be used in order to overcome this problem. In this chapter, we proposed a new approach that uses fuzzy c-means techniques for clustering, type-1 fuzzy functions approach for fuzzy system modelling and subsampling bootstrap method for probabilistic inference. By means of the proposed method, researchers can obtain forecast distribution, forecasts can be obtained from the distribution of forecasts as a measure of central tendency, and combine many different forecast results. For experimental study, we used Istanbul Stock Exchange 100 indices as data sets. For comparison of the results obtained from the proposed method, some other methods that are well known in the literature are used.

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Author information

Authors and Affiliations

  1. Department of Statistics, Forecast Research Laboratory, Giresun University, 28200, Giresun, Turkey

    Ali Zafer Dalar & Erol Eğrioğlu

Authors
  1. Ali Zafer Dalar
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  2. Erol Eğrioğlu
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Correspondence to Ali Zafer Dalar .

Editor information

Editors and Affiliations

  1. Department of Statistics, Arts and Sciences Faculty, Marmara University, Istanbul, Turkey

    Müjgan Tez

  2. Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Dietrich von Rosen

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Dalar, A.Z., Eğrioğlu, E. (2018). Bootstrap Type-1 Fuzzy Functions Approach for Time Series Forecasting. In: Tez, M., von Rosen, D. (eds) Trends and Perspectives in Linear Statistical Inference . Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-73241-1_5

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