Advances in Time Series Forecasting

Advances in Time Series Forecasting

Volume: 2

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting ...
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Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix

Pp. 111-126 (16)

Cagdas Hakan Aladag and Hilal Guney

Abstract

Stock exchanges forecasting is a popular research topic that is attracting more and more attention from researchers and practitioners. Since it is a well-known fact that stock exchanges time series include uncertainty, using conventional time series methods can lead to misleading results. Therefore, a proper approach should be employed for analysis according to the nature of the data. In the literature, fuzzy time series models have been successfully used to forecast real world time series which include vagueness. In order to handle uncertainty in stock exchanges, fuzzy time series forecasting approach proposed by Tsaur [17] is utilized in this study. Fuzzy time series forecasting model suggested by Tsaur [17] uses Markov chain transition matrix for fuzzy inference. In the implementation, the fuzzy time series forecasting model is applied to index 100 in stocks and bonds exchange market of İstanbul in order to show the performance of the model. It is seen that the forecasting model gives accurate forecasts for the data.

Keywords:

Fuzzy inference, Fuzzy relations, Fuzzy time series, Markov chain transition matrix, Number of fuzzy set, Stock exchanges.

Affiliation:

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.