A refined fuzzy time series model for stock market forecasting

Tahseen Ahmed Jilani and Syed Muhammad Aqil Burney

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 12, 2857-2862

Abstract: Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.

Keywords: Frequency-density-based partitioning; Fuzzy time series; Fuzzy logical relationship groups (FLRGs); Fuzzy aggregation operations; Heuristic trend estimation (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:12:p:2857-2862

DOI: 10.1016/j.physa.2008.01.099

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