Modeling and forecasting financial time series with ordered fuzzy candlesticks

The goal of the paper is to present an experimental evaluation of fuzzy time series models which are based on ordered fuzzy numbers to predict financial time series. Considering this approach the financial data is modeled using Ordered Fuzzy Numbers (OFNs) called further by Ordered Fuzzy Candlestick...

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Bibliographic Details
Published in:Information sciences 2014-07, Vol.273, p.144-155
Main Authors: Marszałek, A., Burczyński, T.
Format: Article
Language:English
Subjects:
Directional predictability
Financial time series
Fuzzy autoregressive
Ordered fuzzy number
Stock return
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Summary:The goal of the paper is to present an experimental evaluation of fuzzy time series models which are based on ordered fuzzy numbers to predict financial time series. Considering this approach the financial data is modeled using Ordered Fuzzy Numbers (OFNs) called further by Ordered Fuzzy Candlesticks (OFCs). The use of them allows modeling uncertainty associated with financial data and maintaining more information about price movement at assumed time interval than comparing to commonly used price charts (e.g. Japanese Candlestick chart). Thanks to well-defined arithmetic of OFN, one can construct models of fuzzy time series, such as an Ordered Fuzzy Autoregressive Process (OFAR), where all input values are OFC, while the coefficients and output values are arbitrary OFN; in the form of classical equations, without using rule-based systems. In an empirical study ordered fuzzy autoregressive models are applied to modeling and predict price movement of futures contracts on Warsaw Stock Exchange Top 20 Index.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2014.03.026