Titlebook: Applications of Soft Computing in Time Series Forecasting; Simulation and Model Pritpal Singh Book 2016 Springer International Publishing S

公豬

Introduction,As the application of information technology is growing very rapidly, data in various formats have also proliferated over the time.

novelty

吝嗇性

一回合

乳白光

High-Order Fuzzy-Neuro Time Series Forecasting Model,f forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new “Re-Partitioning Discretization (RPD)” approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the m

未開化

Two-Factors High-Order Neuro-Fuzzy Forecasting Model, In this chapter, we introduce a model to deal with forecasting problems of two-factors. The proposed model is designed using FTS and ANN. In a FTS, the length of intervals in the universe of discourse always affects the results of forecasting. Therefore, an ANN based technique is employed for deter

試驗(yàn)

FTS-PSO Based Model for M-Factors Time Series Forecasting,n 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 adj

TOM

xanthelasma

https://doi.org/10.1007/978-3-322-85564-0also identified various domains specific problems and research trends, and try to categorize them. The chapter ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

interference

https://doi.org/10.1007/978-3-322-85564-0st. Therefore, in this model, it is recommended to consider the repeated FLRs during forecasting. It is also suggested to assign weights on the FLRs based on their severity rather than their patterns of occurrences. For this purpose, a new technique is incorporated in the model. This technique deter