Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

Highlights

• We proposed a model based on Type-2 fuzzy time series and particle swarm optimization.

• The model deals with M-factors time series data set.

• The model is tested and validated with SBI and Google stock index data sets.

• Various comparison studies with different models exhibit superiority of our model.

Abstract

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.