By Pritpal Singh
This e-book studies on an in-depth research of fuzzy time sequence (FTS) modeling. It reports and summarizes earlier learn paintings in FTS modeling and likewise presents a quick advent to different soft-computing thoughts, comparable to synthetic neural networks (ANNs), tough units (RS) and evolutionary computing (EC), concentrating on how those innovations might be built-in into assorted stages of the FTS modeling method. specifically, the e-book describes novel tools 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 might be effectively utilized within the context of predicting Indian summer time monsoon rainfall. because of its easy-to-read type and the transparent causes of the versions, the ebook can be utilized as a concise but entire reference consultant to fuzzy time sequence modeling, and should be invaluable not just for graduate scholars, but additionally for researchers and pros operating for educational, enterprise and executive organizations.
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Extra info for Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques
Appl Math Comput 186(1):330–339 Singh SR (2007c) A simple time variant method for fuzzy time series forecasting. Cybernetics and Systems: An International Journal 38(3):305–321 Singh SR (2008) A computational method of forecasting based on fuzzy time series. Math Comput Simul 79(3):539–554 Singh SR (2009) A computational method of forecasting based on high-order fuzzy time series. Expert Syst Appl 36(7):10,551–10,559 Sivanandam SN, Deepa SN (2007) Principles of soft computing. , New Delhi Song Q, Chissom BS (1993a) Forecasting enrollments with fuzzy time series - Part I.
In this model, researchers employ the four changes as: (i) Using triangular fuzzy set with indeterminate legs and optimizing these triangular fuzzy sets. , (ii) Using indeterminate coefficient in calculating Type-2 forecasting. , (iii) Using center of gravity defuzzifier. , and (iv) Using 4-order Type-2 FTS. This improvement is applied in Step 5 of Algorithm 2. • Lertworaprachaya et al. (Lertworaprachaya et al. 2010) model: Based on these articles (Huarng and Yu 2005; Singh 2007b), a novel high-order Type-2 FTS model is proposed in this article (Lertworaprachaya et al.
And an ). Then, define n linguistic variables A1 , A2 , . . 5/a2 + 0/a3 + . . 5/a3 + . . 5/a2 + 1/a3 + . . 1) .. An = 0/an + 0/a2 + 0/a3 + . . 5/an−1 + 1/an . Then, we obtain the degree of membership of each time series value belonging to each Ai . Here, maximum degree of membership of fuzzy set Ai occurs at interval ai , and 1 ≤ i ≤ n. Then, each historical time series value is fuzzified. For example, if any time series value belongs to the interval ai , then it is fuzzified into Ai , where 1 ≤ i ≤ n.
Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques by Pritpal Singh