FTS-PSO Based Model for M-Factors Time Series Forecasting

  • Pritpal SinghEmail author
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 330)


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 chapter, we have intended to introduce a new Type-2 FTS model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing 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 FTS models and conventional time series models.


FTS Stock index forecasting Type-1 Type-2 PSO Defuzzification 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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