Table 4 summarizes selected parameters from the deterministic trend model.
Thus, in the present study, a stochastic trend model based on linear fitting is used.
Therefore, we can explicitly fit the trend model of the EM flight and the EF flight.
The multivariate trend model is fitted using the Prais Winsten recursive autoregression methodology.
Linear trend model for electricity consumption is also developed as a function of temperature.
We introduced the low-frequency trend model into the basis pursuit inversion implementation.
In the improved method, the low-frequency trend model as prior knowledge stabilized the inversion and improved the lateral continuity because the low-frequency trend model is continuous in the space axis.
The corrected strain is expressed by using the following first-order trend model (Kitagawa and Gersch, 1984).
Open image in new window Fig. 5 The comparison of fit of multivariate time trend model with the actual value.
Therefore, we will use transcendental time functions as LF predictors to control for periodic autocorrelation in our multivariate trend model.
The software calculated the coefficient of determination as percentage, slope of the linear trend model with direction of the trend.
