By Pritpal Singh

This booklet studies on an in-depth learn of fuzzy time sequence (FTS) modeling. It studies and summarizes past study paintings in FTS modeling and likewise presents a quick creation to different soft-computing concepts, reminiscent of synthetic neural networks (ANNs), tough units (RS) and evolutionary computing (EC), targeting how those recommendations could be built-in into varied stages of the FTS modeling method. particularly, the publication describes novel equipment because of the hybridization of FTS modeling techniques with neural networks and particle swarm optimization. It additionally demonstrates how a brand new ANN-based version will be effectively utilized within the context of predicting Indian summer season monsoon rainfall. due to its easy-to-read sort and the transparent reasons of the types, the publication can be utilized as a concise but complete reference consultant to fuzzy time sequence modeling, and may be helpful not just for graduate scholars, but in addition for researchers and pros operating for educational, company and executive organizations.

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Additional resources for Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques

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A j . Here, each “t” represents the day for corresponding fuzzified time series values. Based on the input and output fuzzified values, the nth-order FLRs are established as: Al , . . , Am , An → A j . , l, . . , j) is used as target output. 4 Hybridize Modeling Approach for FTS 25 (b) For partitioning the Universe of discourse: Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns (Gondek and Hofmann 2007). Time series data are pervasive across all human endeavors, and their clustering is one of the most fundamental applications of data mining (Keogh and Lin 2005).

Then, Type-2 forecasts are obtained from these FLRs. 3 FTS Modeling Approach Chen (1996) proposed a simple calculation method to get a higher forecasting accuracy in FTS model. Still this model is used as the basis of FTS modeling. The basic architecture of this model is depicted in Fig. 2. This model employs the following five common steps to deal with the forecasting problems of time series, which are explained below. Contributions of various research articles in different phases of this model are also categorized in this section.

Knowl Inf Syst 34:171–192 Donaldson RG, Kamstra M (1996) Forecast combining with neural networks. J Forecast 15(1):49– 61 Dote Y, Ovaska SJ (2001) Industrial applications of soft computing: a review. Proc IEEE 89(9):1243– 1265 Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, pp 39–43 Eberhart R, Shi Y (2001) Particle swarm optimization: Developments, applications and resources.

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