Download Applications of Soft Computing in Time Series Forecasting: by Pritpal Singh PDF

By Pritpal Singh

This booklet experiences on an in-depth research of fuzzy time sequence (FTS) modeling. It experiences and summarizes prior learn paintings in FTS modeling and likewise presents a short advent to different soft-computing options, similar to man made neural networks (ANNs), tough units (RS) and evolutionary computing (EC), targeting how those concepts could be built-in into diverse levels of the FTS modeling method. specifically, the publication describes novel tools caused by the hybridization of FTS modeling techniques with neural networks and particle swarm optimization. It additionally demonstrates how a brand new ANN-based version could be effectively utilized within the context of predicting Indian summer time monsoon rainfall. due to its easy-to-read sort and the transparent reasons of the versions, the publication can be utilized as a concise but complete reference consultant to fuzzy time sequence modeling, and may be beneficial not just for graduate scholars, but additionally for researchers and pros operating for tutorial, enterprise and govt organizations.

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

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SC techniques comprise of ANN, RS, EC, and their hybridizations, have recently been employed to solve FTS modeling problems. They endeavor to provide us approximate results in a very cost effective manner, thereby reducing the time complexity. In this survey, a categorization has been presented based on utilization of different SC techniques with the FTS modeling approach along with basic architectures of different hybridized based FTS models. Fuzzy sets are the oldest component of SC, which is known for representation of real time or uncertain events in a linguistic manner, and can take decisions very faster.

2003; Huarng 2001). For example, researchers in these articles (Chen et al 2008; Hsu et al. 2003) consider only closing price in the forecasting of the stock index. However, the stock index price consists of many different observations, such as opening, high, low, etc. If these additional observations are used with one-factor variable, then it is referred to as M-factors FTS model. The model proposed by Huarng and Yu (Huarng and Yu 2005) is based on M-factors, because they use high and low as the secondary-observations to forecast the closing price of TAIEX.

2011; Chen 2002; Chen and Chen 2011a, b; Chen and Chung 2006b; Chen et al 2008; Gangwar and Kumar 2012; Jilani and Burney 2008; Own and Yu 2005; Singh 2007a, c, 2008, 2009; Tsai and Wu 2000). 20 2 Fuzzy Time Series Modeling Approaches: A Review involved in various dynamic processes. On the other hand, to extract rule from the fuzzified time series data set, Qiu et al. (2012) utilized C-fuzzy decision trees (Pedrycz and Sosnowski 2005) in FTS model. , first a new stop condition is introduced to reduce the computational cost, and second weighted C-fuzzy decision tree (WCDT) is introduced where weight distance is computed with information gain.

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