Time Series Models
نویسنده
چکیده
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 are applicable for data that are collected over time. A leading example, to be discussed more fully below, is the rst order autoregression model, for which the dependent variable yt for time period t satis es
منابع مشابه
Overview and Comparison of Short-term Interval Models for Financial Time Series Forecasting
In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...
متن کاملWhich Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
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...
متن کاملFitting of Count Time Series Models on the Number of Patients Referred to Addiction Treatment Centers in Semnan County
Abstract. Count data over time are observed in many application areas. Many researchers use time series patterns to analyze this data. In this paper, the poisson count time series linear models and negative binomials on this type of data with the explanatory variables are studied. The Likelihood analysis and the evaluation of count time series model based on generalized linear models are pres...
متن کاملRainfall-runoff process modeling using time series transfer function
Extended Abstract 1- Introduction Nowadays, forecasting and modeling the rainfall-runoff process is essential for planning and managing water resources. Rainfall-Runoff hydrologic models provide simplified characterizations of the real-world system. A wide range of rainfall-runoff models is currently used by researchers and experts. These models are mainly developed and applied for simulation...
متن کاملEvaluation of SARIMA time series models in monthly streamflow estimation in Idanak hydrometry station
prediction of hydrological variables is a highly effective tool in water resource management. One of the important tools for modeling hydrological processes is the use of time series modeling and analysis. River series production series can be used by time series models in various studies such as drought, flood, reservoir systems design and many other purposes For this purpose, monthly flow dat...
متن کاملAN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
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...
متن کامل