基于模糊时间序列和最长公共/重复子序列的股票预测新方法
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence
摘要：
股票价格预测对投资者来说是一个重要的问题，因为极端准确的预测可以带来高利润。模糊时间序列（FTS）和最长共同/重复子序列（LCS/LRS）是价格预测的两个重要问题。然而，据我们所知，没有显着的研究使用LCS/LRS预测股票价格。价格不可能与历史价格完全相同。因此，本文提出了一种结合FTS和LCS/LRS的股票价格预测方法。这种方法是基于历史会重演的原则。它使用不同的区间长度在FTS的价格模糊化，LCS/LRS寻找相同的模式，在历史价格预测未来的股票价格。在实验中，我们检查各种区间的模糊时间集，以达到高的预测精度。所提出的方法优于传统的方法在预测精度方面，而且，它是易于实现的。
英文摘要：
Stock price forecasting is an important issue for investors since extreme accuracy in forecasting can bring about high profits. Fuzzy Time Series (FTS) and Longest Common/Repeated Sub-sequence (LCS/LRS) are two important issues for forecasting prices. However, to the best of our knowledge, there are no significant studies using LCS/LRS to predict stock prices. It is impossible that prices stay exactly the same as historic prices. Therefore, this paper proposes a state-of-the-art method which combines FTS and LCS/LRS to predict stock prices. This method is based on the principle that history will repeat itself. It uses different interval lengths in FTS to fuzzify the prices, and LCS/LRS to look for the same pattern in the historical prices to predict future stock prices. In the experiment, we examine various intervals of fuzzy time sets in order to achieve high prediction accuracy. The proposed method outperforms traditional methods in terms of prediction accuracy and, furthermore, it is easy to implement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
