A high-order fuzzy time series forecasting model for internet stock trading

• A novel high-order fuzzy time series model is applied to predict stock prices.
• The paper uses entropy-based partitioning to define the linguistic intervals.
• The paper applies an artificial neural network to compute the complicated FLRs.
• The paper uses adaptive expectation model to adjust the defuzzification procedure.
• The empirical results outperform the hybrid fuzzy time series models.

Recently, many fuzzy time series models have already been used to solve nonlinear and complexity issues. However, first-order fuzzy time series models have proven to be insufficient for solving these problems. For this reason, many researchers proposed high-order fuzzy time series models and focused on three main issues: fuzzification, fuzzy logical relationships, and defuzzification. This paper presents a novel high-order fuzzy time series model which overcomes the drawback mentioned above. First, it uses entropy-based partitioning to more accurately define the linguistic intervals in the fuzzification procedure. Second, it applies an artificial neural network to compute the complicated fuzzy logical relationships. Third, it uses the adaptive expectation model to adjust the forecasting during the defuzzification procedure. To evaluate the proposed model, we used datasets from both the Taiwanese stock index from 2000 to 2003 and from the student enrollment records of the University of Alabama. The results of our study show that the proposed model is able to obtain an accurate forecast without encountering conventional fuzzy time series issues.