Stock market forecasting by using a hybrid model of exponential fuzzy time series

• In this study a two phase approach is proposed based on exponential fuzzy time series and learning automata.
• In the first phase, the optimal lengths of intervals are estimated by applying LA based EAs in training set.
• Second phase aim is to estimate certain adjusting parameters for minimizing errors in training set.
• The conventional FTS in the first phase is applied and in the second phase EFTS is employed.
• Forty six case studies from five stock index databases are employed in extensive experiments.

The initial aim of this study is to propose a hybrid method based on exponential fuzzy time series and learning automata based optimization for stock market forecasting. For doing so, a two-phase approach is introduced. In the first phase, the optimal lengths of intervals are obtained by applying a conventional fuzzy time series together with learning automata swarm intelligence algorithm to tune the length of intervals properly. Subsequently, the obtained optimal lengths are applied to generate a new fuzzy time series, proposed in this study, named exponential fuzzy time series. In this final phase, due to the nature of exponential fuzzy time series, another round of optimization is required to estimate certain method parameters. Finally, this model is used for future forecasts. In order to validate the proposed hybrid method, forty-six case studies from five stock index databases are employed and the findings are compared with well-known fuzzy time series models and classic methods for time series. The proposed model has outperformed its counterparts in terms of accuracy.