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 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.
It is not the answer that enlightens, but the question.
By E. I. Decouvertes (1909–1994)
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Singh, P. (2016). FTS-PSO Based Model for M-Factors Time Series Forecasting. In: Applications of Soft Computing in Time Series Forecasting. Studies in Fuzziness and Soft Computing, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-319-26293-2_6
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