A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence

TL;DR

This paper introduces a new stock forecasting method combining Fuzzy Time Series and Longest Common/Repeated Sub-sequence techniques, leveraging historical pattern repetition to improve prediction accuracy.

Contribution

It is the first to integrate LCS/LRS with FTS for stock prediction, enhancing accuracy and simplicity over traditional methods.

Findings

Outperforms traditional forecasting methods in accuracy

Effective use of multiple interval lengths in FTS

Easy to implement and adapt

Abstract

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.