A refined fuzzy time series model for stock market forecasting
A refined fuzzy time series model for stock market forecasting
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DOI：
10.1016/j.physa.2008.01.099
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发表时间：
2008-05
影响因子：
3.3
通讯作者：
T. Jilani;S. Burney
中科院分区：
文献类型：
--
作者：
T. Jilani;S. Burney
时间序列模型已被用于股票价格、学术招生、天气、交通事故伤亡等的预测。本文提出了一种简单的时变模糊时间序列预测方法。该方法使用启发式方法来定义基于频率密度的语篇划分。我们提出了一种模糊度量来使用基于频率密度的划分。建议的模糊指标还使用趋势预测值来计算预测值。将该方法应用于阿拉巴马大学的TAIEX预测和招生预测。结果表明，与其他模糊时间序列预测方法相比，该方法具有更高的预测精度。
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.