Abstract
FTS forecasting method has been applied in several domains, such as stock market price, temperature, sales, crop production, and academic enrollments. 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 determining the intervals of the historical time series data sets by clustering them into different groups. The historical time series data sets are then fuzzified, and the high-order FLRs are established among fuzzified values based on FTS method. The chapter also introduces some rules for interval weighing to defuzzify the fuzzified time series data sets. From experimental results, it is observed that the proposed model exhibits higher accuracy than those of existing two-factors FTS models.
The essence of mathematics is not to make simple things complicated, but to make complicated things simple
By S. Gudden
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Singh, P. (2016). Two-Factors High-Order Neuro-Fuzzy Forecasting Model. 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_5
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DOI: https://doi.org/10.1007/978-3-319-26293-2_5
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