Sentence examples for moving average parts from inspiring English sources
The model is generally referred to as an ARIMA p,d,q), where p, d, and q are integers greater than or equal to zero and refer to the order of the autoregressive, integration (number of differencing steps needed to achieve stationarity), and moving average parts of the model, respectively: ϕ ( B ) ( 1 − B ) d X t = θ ( B ) Z t. (13).
The acronym ARIMA stands for "Auto-Regressive Integrated Moving Average", where p refers to the autoregressive, d the integrated, and q the moving average parts of the time series model.
The model is usually then referred to as the ARMA p,q) model, where p is the order of the autoregressive part and q is the order of the moving average part.
The model was defined with an autoregressive part of order p, a moving average part of order q, a seasonal-autoregressive part of order P, a seasonal-moving average part of order Q, differencing and seasonal-differencing orders d and D, and periodic variable n.
An ARMA (1,1) model comprises an autoregressive part and a moving average part and expresses each observation as a combination of the two: X t=ε t+X t−1 +ε t−1 where X t is the symptom severity score at time t, ε t is the error term.
The ARMA model consists of an autoregressive (AR) and a moving average (MA) parts.
Lattice reflection coefficients for autoregressive (AR) and moving average (MA) parts are simultaneously computed.
The proposed algorithm is derived for ARX system only, but it allows for future reformulation for a general ARMAX system with known moving average (MA) part.
An iterative process of eliminating non-significant terms, and identifying further autoregressive (AR) or moving average (MA) terms for parts of the model remaining unexplained, determined the most parsimonious LTF model.
Figure 9 Moving average of H2S (lower part of the picture) and SO2concentration values.
The relatively low GDR scores (∼70%) for shorter seizures can be attributed in part to the moving average filter length.
