Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes
نویسندگان
چکیده
منابع مشابه
Wind speed forecasting based on autoregressive moving average- exponential generalized autoregressive conditional heteroscedasticity-generalized error distribution (ARMA-EGARCH-GED) model
With the increase of wind power as a renewable energy source in many countries, wind speed forecasting has become more and more important to the planning of wind speed plants, the scheduling of dispatchable generation and tariffs in the day-ahead electricity market, and the operation of power systems. However, the uncertainty of wind speed makes troubles in them. For this reason, a wind speed f...
متن کاملSome Autoregressive Moving Average Processes with Generalized Poisson Marginal Distributions
Abstrac t . Some simple models are introduced which may be used for modelling or generating sequences of dependent discrete random variables with generalized Poisson marginal distribution. Our approach for building these models is similar to that of the Poisson ARMA processes considered by Al-Osh and Alzaid (1987, J. Time Ser. Anal., 8, 261-275; 1988, Statist. Hefte, 29, 281-300) and McKenzie (...
متن کاملDissertation Time - Frequency - Autoregressive - Moving - Average Modeling of Nonstationary Processes
This thesis introduces time-frequency-autoregressive-moving-average (TFARMA) models for underspread nonstationary stochastic processes (i.e., nonstationary processes with rapidly decaying TF correlations). TFARMAmodels are parsimonious as well as physically intuitive and meaningful because they are formulated in terms of time shifts (delays) and Doppler frequency shifts. They are a subclass of ...
متن کاملChapter 3: Autoregressive and moving average processes
2 Moving average models Definition. The moving average model of order q, or MA(q), is defined to be Xt = t + θ1 t−1 + θ2 t−2 + · · ·+ θq t−q, where t i.i.d. ∼ N(0, σ). Remarks: 1. Without loss of generality, we assume the mean of the process to be zero. 2. Here θ1, . . . , θq (θq 6= 0) are the parameters of the model. 3. Sometimes it suffices to assume that t ∼WN(0, σ). Here we assume normality...
متن کاملStationarity of Generalized Autoregressive Moving Average Models
Time series models are often constructed by combining nonstationary effects such as trends with stochastic processes that are believed to be stationary. Although stationarity of the underlying process is typically crucial to ensure desirable properties or even validity of statistical estimators, there are numerous time series models for which this stationarity is not yet proven. A major barrier...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: American Journal of Applied Sciences
سال: 2015
ISSN: 1546-9239
DOI: 10.3844/ajassp.2015.896.901