in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...

Grey theory [1] has been proposed over 30 years. Great endeavor has been devoted to increase the forecasting precision. One of methods treated the forecasting error to become the modified grey forecasting model. Hsu and Wen [2] modified original GM (1,1) models are improved by using residual modifications with Markov chain sign estimations. Hsu and Chen [3] improved grey GM (1,1) model, using a...

We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental ...

Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of...

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) ...

One of the yet unresolved engineering problems is forecasting the creep lives of weldment in a pragmatic way with sufficient accuracy. There are number of obstacles to circumvent including: complex material behavior, lack of accurate knowledge about the creep material behavior specially about the heat affected zones (HAZ),accurate and multi-axial creep damage models, etc. In general, creep life...

The existing techniques of forecasting a future count either treat the time series of counts as a Gaussian time series or use a random effects based dynamic Poisson model. The normality based approach may not yield valid forecasting, whereas the random effects based model usually generates a complex correlation structure for the time series of counts which may be impractical to use for forecast...

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

The fast and accurate forecasting method can help makers to make appropriate strategy. Zadeh was given the definition of a fuzzy set in 1965. Song and Chissom proposed the definition and the forecasting framework of fuzzy time series in 1993. Sullivan and Woodall first proposed the forecasting method to handle one factor with probability Markov model in 1994. Li and Cheng proposed a stochastic ...