Moving-average representation of autoregressive approximations
نویسندگان
چکیده
منابع مشابه
Moving - Average Representation of Autoregressive Approximations
We study the properties of an MA1-representation of an autoregressive a p p r o x-imation for a stationary, real-valued process. In doing so we g i v e an extension of Wiener's Theorem in the deterministic approximation setup. When dealing with data, we can use this new key result to obtain insight i n to the structure of MA1-representations of tted autoregressive models where the order increas...
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 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 کاملForecasting Inflation: Autoregressive Integrated Moving Average Model
This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...
متن کاملComparison of autoregressive integrated moving average (ARIMA) model and adaptive neuro-fuzzy inference system (ANFIS) model
Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...
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: Stochastic Processes and their Applications
سال: 1995
ISSN: 0304-4149
DOI: 10.1016/0304-4149(95)00061-5