ARIMA在极度崩溃市场中的局限性:一种建议的方法

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI:10.1109/ECACE.2019.8679216

Md. Mujibur Rahman Majumder, M. Hossain

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引用次数: 5

摘要

由于股票价格令人费解的性质，股票市场的预测是一项复杂的任务。影响股票价格的因素多种多样，导致投资者难以预测股票的性质。研究人员提出了多种方法来预测即将到来的股票价格，通过计算股票的性质和内部和外部因素。自回归综合移动平均(ARIMA) Box-Jekins方法是一种基于先验时间序列数据预测股票未来价值的方法。但是在实验中，我们发现了一个突破点，ARIMA在预测中显示出不稳定的行为，即它返回3种类型的值(固定，负和正)。这是在股票价格极度崩溃之后发生的。为了解决这一问题，我们提出了一种新的模型，该模型也可以根据以前的价格预测未来的价格，但精度高于ARIMA，并且还解决了ARIMA中出现的负固定值预测问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Limitation of ARIMA in extremely collapsed market: A proposed method

The prediction of equity market is a perplexing task because of puzzling nature of the stock price. A large variety of factors influence the price of stocks that causes the investors in trouble to predict the nature of stock. Researchers proposed various methods to forecast the upcoming price of stocks by figuring out the nature of stock and by computing internal and external factors. Auto Regressive Integrated Moving Average (ARIMA) Box-Jekins method is one of the eminent methods that forecast the future value of stock based on previous time series data. But while experimenting, we have found a crack point where ARIMA showed unstable behavior i.e. it returned 3 types of values (fixed, negative and positive) in prediction. This was after the prices of stock extremely collapsed. To solve this problem, we have proposed a new model that also predicts the future prices from previous prices but obtained greater accuracy than the ARIMA and also solve the negative and fixed value prediction problem occurred in ARIMA.

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2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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