Sentence examples for moving average coefficient from inspiring English sources

The residuals are obtained starting from the same model (nabla hbox {TICD}_{t}=e_{t}+0.4995e_{t-1}), by writing (e_{t}=nabla hbox {TICD}_{t}-0.4995e_{t-1}) where the cell V49 contains the moving average coefficient 0.4995.

To provide additional information related with the discussion, two new set of data are worked out: (1) significant wave height obtained in the Gulf of Cádiz (hindcast data, WANA 1952046) with extremal index 0.32, and (2) a simulated series with an ARMA model, autoregressive coefficient 0.95, zero moving average coefficient and an extremal index equal to 0.3.

The null hypothesis of interest is (H_{0}:psi _{f}=0), so that the model under the null is a univariate Ar(1)  white noise process, while the signal under the alternative is an Arma 1, 1) instead with moving average coefficient (psi _{f}).

The null hypothesis of interest is (H_{0}:delta _{f}=0), so that the model under the null is still a random walk signal plus white noise, while the signal under the alternative is an Ima 1, 2) with second moving average coefficient (delta _{f}).

The null hypothesis of interest is (H_{0}:psi _{f}=0), so that the model under the null is still a random walk signal plus white noise, while the signal under the alternative is an Ima 1, 1) with moving average coefficient ( psi _{f}).

where ϕ(L =1−ϕ 1 L−ϕ 2 L 2−ϕ 3 L 3−⋯−ϕ p L p and θ(L =1−θ 1 L−θ 2 L 2−θ 3 L 3−⋯−θ p L q. (B^_{l}) is the correlated normally distributed random variable, e l is an uncorrelated Gaussian noise, θ l is moving average coefficient, and L is the lag operator.

We estimated the ARIMA (autoregressive, seasonality, and moving average) coefficients for the four series in each of the three study hospitals.

Three- to 12-day moving average coefficients of NO2 were 50 400% greater than individual-day lag coefficients and steadily increased in magnitude as the size of the moving average increased; however, the ORs for moving averages were much smaller, given the smaller absolute increment represented by a 1-day increase in the longer moving averages.

Denoting the first K autocorrelations as Open image in new window (k = 1,2,… K) Box and Pierce [6] showed that if the fitted model is appropriate then Open image in new window (13)is approximately distributed as χ2 (K p q) where n is the number of residuals used to fit the model, p and q are the number of the autoregressive (AR) and moving average (MA) coefficients.

Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E).

Once a usage history is established, different prediction models (e.g., autoregressive (AR), autoregressive moving average (ARMA) with time varying coefficients, autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and so on [33]) can be used to forecast the future PU traffic pattern.