improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

understanding the genetic regulatory networks, the discovery of interactions between genes, and understanding regulatory processes in a cell at the gene level, is one of the major goals of system biology and computational biology. modeling gene regulatory networks, describing the actions of the cells at the molecular level and is used in medicine and molecular biology applications such as metab...

data with high frequency have a particular type of none stationary that is called fractional none stationary. this property causes the emergence of long-term memory in financial time series with high frequency. the existence of long-term memory in cement industry time-series is studied in this paper at first and its presence will be confirmed in a high confidence level by two tests r/s and gph....

New dynamic Bayesian models for survival data analysis are applied in a study of contributory factors to unemployment. The models treat survival data as time series data, reflecting the need to model time varying relationships with explanatory variables that cannot be accommodated within standard proportional hazard models. In the present study, prior expectations that the effects of various so...

We examine the joint time series of option prices and returns on the S&P 500 index and a set of stocks drawn from the index with a new arbitrage-free multivariate stochastic volatility model that captures a market effect. The preliminary results show that the new model fits well for all the marginal time series for different periods of time. The price of volatility risk is estimated from option...

Time series data measure phenomena when there are dependencies over time between prior and current values of the observations. This chapter provides a brief introduction to the standard (linear autoregressive moving-average) time series models including how such models are identified and fitted to the data. A review of more generalized models follows. This includes bilinear models which are use...

Overview In contrast to the classical linear regression model, in which the components of the dependent variable vector y are not identically distributed (because its mean vector varies with the regressors) but may be independently distributed, time series models have dependent variables which may be identically distributed, but are typically not independent across ovbservations. Such models ar...