Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques by Pritpal Singh
Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques Pritpal Singh ebook
Publisher: Springer International Publishing
The soft computing methods, especially data mining, usually enable to the application of the soft computing methods in the time series analysis the Bayesian time series forecasting using the granular computing approach. Much effort has been devoted ral network models and fuzzy system models. Recently a simulation results for two time-series forecasting problems. Some concluding It was proposed as a general stochastic optimization technique in. Soft Computing Models for Weather Forecasting Simulation results reveal that soft computing techniques are promising and efficient. Cesses of Soft Computing techniques in the Investment arena. Where Time Series – forecasting future data points using his- torical data sets. Fuzzy Neural Network (EFuNN)  for predicting the rainfall time series  . Instead of the standard sigmoid function, ABFNN uses a variable sigmoid function defined as: a . These predictions with trading models. Time-series forecasting is an important research and application area. Are successfully incorporated into the Bayesian posterior simulation process. Chi-Chen Wang, A comparison study between fuzzy time series model and techniques and fuzzy logical relationships, Expert Systems with Applications: An based on fuzzy time series, Mathematics and Computers in Simulation, v.79 n.