#DOUBLE MOVING AVERAGE EVIEWS 9 SERIES#

Non-seasonal ARIMA models are generally denoted ARIMA( p, d, q) where parameters p, d, and q are non-negative integers, p is the order (number of time lags) of the autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), and q is the order of the moving-average model. The purpose of each of these features is to make the model fit the data as well as possible. The I (for 'integrated') indicates that the data values have been replaced with the difference between their values and the previous values (and this differencing process may have been performed more than once). The MA part indicates that the regression error is actually a linear combination of error terms whose values occurred contemporaneously and at various times in the past. The AR part of ARIMA indicates that the evolving variable of interest is regressed on its own lagged (i.e., prior) values. pure sine or complex-valued exponential process ), the predictable component is treated as a non-zero-mean but periodic (i.e., seasonal) component in the ARIMA framework so that it is eliminated by the seasonal differencing.

#DOUBLE MOVING AVERAGE EVIEWS 9 SERIES#

Note that if the time series contains a predictable sub-process (a.k.a. purely nondeterministic ) wide-sense stationary time series, we are motivated to make stationary a non-stationary time series, e.g., by using differencing, before we can use the ARMA model. Since the ARMA model, according to the Wold's decomposition theorem, is theoretically sufficient to describe a regular (a.k.a. When the seasonality shows in a time series, the seasonal-differencing could be applied to eliminate the seasonal component. ARIMA models are applied in some cases where data show evidence of non-stationarity in the sense of mean (but not variance/ autocovariance), where an initial differencing step (corresponding to the 'integrated' part of the model) can be applied one or more times to eliminate the non-stationarity of the mean function (i.e., the trend).

Both of these models are fitted to time series data either to better understand the data or to predict future points in the series ( forecasting). In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model.

Double moving average eviews 9