Neuro-fuzzy inference system (ASuPFuNIS) model for intervention time series prediction of electricity prices

This paper presents an approach to time series prediction based on Asymmetric Subsethood-Product Fuzzy Neural Inference System (ASuPFuNIS). The standard time series techniques have standard averaging where a fixed weight is added to the past values. In this paper we present a novel neuro-fuzzy inference system based on asymmetric subsethood with intervention based transfer function based time series model for accurate prediction of time series. The design of the model is described, and the scheme is evaluated by application to real-world problem of cost of electricity prices over a period of seven year in Ontario, Canada. We also study the various statistical properties of the data.

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