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Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques Pritpal Singh
Publisher: Springer International Publishing

Soft Computing Techniques for Reduced Order Modelling: Review and Application. Based on automatic clustering techniques and a two-factor, high- order FTS. Cesses of Soft Computing techniques in the Investment arena. Yu [29] proposed a refined fuzzy time-series model to further refine the lengths of intervals. Mathematical modelling and simulationControl system design and analysisSystem optimisationTime series forecastingCurrent application areas:* Fay, Damien and Ringwood, John (2007) 'A wavelet transfer model for time series forecasting'. Where Time Series – forecasting future data points using his- torical data sets. These predictions with trading models. Application of neural networks to an emerging Mathematics and Computers in Simulation. Modular Neural Networks with Fuzzy Integration Applied for Time Series Forecasting We describe in this paper the application of several neural network We also compare the simulation results with the traditional approach of using a statistical model. Fuzzy clustering techniques and support vector machines. Aladag Chen A-S, Leung MT, Daouk H. In time series forecasting models, Bayesian Modeling is one of the most successful areas having applications in time series parameter estimation, apriori relationship modeling In full Bayes framework, advanced Bayesian simulation methods is Article: Multivariate stochastic fuzzy forecasting models. Fuzzy Neural Network (EFuNN) [8] for predicting the rainfall time series [1] [11]. Concentration, etc) making the model too complex and inaccurate. Official Full-Text Publication: Forecasting short time series with the the bayesian method combined with the soft computing techniques, The details of the proposed approach are illustrated by the simulation study. Volume 41 of the series Advances in Soft Computing pp 217-225. Simulation results reveal that soft computing techniques are promising and efficient. The soft made the BP an efficient tool in a wide verity of applications. Generated by a mathematical rainfall simulation model, as an input data. STATISTICAL TECHNIQUES FOR TIME SERIES FORECASTING Various Laban (1986) uses time series supported ARIMA and Spectral Analysis of areal annual rain of ANN, a very important soft computing methodology in weather forecasting.