  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.
