Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization |
| |
Institution: | 1. School of Mathematics and Computer Applications, Thapar University, Patiala-04, Punjab, India;2. Department of Computer Science and Engineering, Tezpur University, Tezpur-28, Assam, India |
| |
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. |
| |
Keywords: | Stock index forecasting Type-1 fuzzy time series Type-2 fuzzy time series Particle swarm optimization Defuzzification |
本文献已被 ScienceDirect 等数据库收录! |
|