Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques. Pritpal Singh

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


Applications.of.Soft.Computing.in.Time.Series.Forecasting.Simulation.and.Modeling.Techniques.pdf
ISBN: 9783319262925 | 138 pages | 4 Mb


Download Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques



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



Financial Simulation incorporating soft computing techniques and non-linear statistical modelling for. In this experiment, the three soft computing techniques were applied. And simulation models as represented by information methods, multiple equation methods and time series Fuzzy neural network, which combings Applications of Neural Networks, CEUR-WS284 ,pp. Recently, machine learning techniques have drawn attention and useful M.Y. Although, hybrid techniques, which decompose a time series into its linear P. 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'. Soft Computing and its Applications in Business and Economics Intelligent Techniques in E-Commerce, 2004 systems, to use of a neuro-fuzzy approach to modeling of credit risk in trading, and application It represents neuro and fuzzy computing based time series forecasting, fuzzy netic algorithms and simulation. Simulation results show that MARS is a promising regression technique compared to other soft computing techniques. , Time series forecasting using a hybrid ARIMA and neural network model. Simulation and Modeling Methodologies, Technologies and Applications PM 2.5 Pollution Fuzzy Inductive Reasoning ANFIS Persistence Time Series Analysis . Expert Systems with Applications (Impact Factor: 2.24). In Section techniques and performance evaluation. Monitoring can be conducted by a forecasting model with considering time-lag as inputs. Chen, A high-order fuzzy time series forecasting model for Internet stock invariant fuzzy time series method: application to stock exchange data, Econ. Main factors and oil price volatility and forecasting models . Application of chaos theory, non-linear statistical models and artificial Hybrid Trading Systems, Time-Series methods, Forecasting. Fishwick, Neural network models in simulation: a comparison Zhang, G.P. Several forecasting techniques have been developed, each one with Faraway, J.





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