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The multivariate trend model is fitted using the Prais Winsten recursive autoregression methodology.
Then the paper applied multivariate trend model to develop a forecasting model of the load factors of the regional flights.
Therefore, we will use transcendental time functions as LF predictors to control for periodic autocorrelation in our multivariate trend model.
Since we have no common exogenous input for the EM and EF flights, we fit multivariate trend model.
Therefore, in order to control the periodic autocorrelation, we introduce transcendental time functions as predictors of the load factor in the multivariate trend model.
The fit of the multivariate trend model of the LF of the EM and EF flights is given in Table 8.
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Thus, rather than other models, for example panel data regression model, we apply multivariate trend models.
Open image in new window Fig. 5 The comparison of fit of multivariate time trend model with the actual value.
It is hence recommended to jointly apply univariate and multivariate trend tests in order to capture all existing trend components and guide the user towards the appropriate models.
Global mean trend models.
In the multivariate models, a trend to lower probability of unplanned 72-h revisits was observed at occupancy ≥105% compared to occupancy <95% (OR 0.88, CI 0.76 to 1.01).
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