Sentence examples for moving average terms from inspiring English sources

In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function.

Results indicate that dual memory models offer better representation of the original time-series than classical models; further, forcing the differentiation parameter of ARIMA model to equal 1 leads to over-inflated moving average terms and, consequently, to questionable models with artificial correlation structures.

The notation ARMA (p, q) refers to the model with p autoregressive terms and q moving average terms.

Adding and subtracting autoregressive and moving average terms did not change the statistical insignificance of the law in these models.

The same is true when we add moving average terms, remove temperature and included disposable income in the model.

Our final SARIMA model contained first-order (p = 0.0155) and second-order (p = 0.0167) moving average terms, a first-order seasonal (period of 12) moving average term (p = 0.0169), and an upward linear time trend (p < 0.0001).

The power of an ARIMA model resides in that it can incorporate all the AR term, the integrated term, and the moving average term together to model time series with a wide variety of features such as trend by simply adjusting the parameters of each term.

However, the significant lags between lag 450 and lag 500, as shown in Fig. 4 are randomly distributed without a main large spike that decreases after a few lags or follow by a damped wave which can present a moving average term or autocorrelation pattern.

The orders p k = 1 and q k = 1, correspond to a single autoregressive and moving average term on the daily scale, equivalent to observing an autoregressive process of first order with measurement error ([ 19], Exercise 2.9).

For the final ARIMA model, we found that the ARIMA (1,0,3) model was the most suitable, with an auto-regression term of 1 and a moving average term of 3. The correlogram indicated that there was a significant autocorrelation out to about 3 lags, and this autocorrelation decayed slowly over time (figure 1).

With a seasonal period of seven days (s = 7), we set P k = 1 and Q k = 1, so that the random seasonal component is a combination of an autoregressive and moving average term, each of first order over a period of seven days.