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A new procedure in stock market forecasting based on fuzzy random auto-regression time series model

机译：基于模糊随机自动回归时间序列模型的股票市场预测的新程序

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Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data preparation procedure of triangular fuzzy number to build an improved fuzzy random auto regression model using non-stationary stock market data for forecasting purposes. The improved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variability and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidence intervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve accuracy of the fuzzy random auto-regression model. (C) 2018 Elsevier Inc. All rights reserved.
机译：根据数据准备，预测方法，绩效评估和性能措施，股票市场预测中使用的各种模型已被分类。然而，这些模型在数据准备中没有充分讨论以克服随机性，以及股票价格在实现高预测准确性方面的不确定性和波动性。因此，本文的焦点是三角模糊数的数据准备过程，以使用非静止股票市场数据来预测预测目的的改进的模糊随机自动回归模型。改进的预测模型考虑了两种类型的输入，这是股票市场价格低高和单点值的数据。尽管如此，低高数据本质上存在可变性和波动性，但单个数据必须在对称左右扩展到呈现可变性和标准误差。然后，预期和差异，模糊随机数据的置信区间被构造用于模糊输入输出数据。通过使用输入输出数据和单纯x方法，可以估计模型的参数。在这项研究中，一些实际数据集用于代表两种类型的投入，这是吉隆坡证券交易所和阿拉巴马州大学招生。该研究发现，可变性和传播调整是数据准备中的重要因素，以提高模糊随机自动回归模型的准确性。 （c）2018年Elsevier Inc.保留所有权利。

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