New approach of the ann methodology for forecasting time series: use of time index

In previous publications, the authors reported their work with the artificial neural networks (ANN) methodologies for the forecast of guest nights in hotels time series. The ANN methodology has made predictions more accurate than other methodologies [1, 5]. However, as a consequence of the tourism demand increase in the last years these time series registered an unusual increase in its values. Considering that the ANN methodology uses the past to predict the future in a statistically way, it became very difficult for the ANN to predict numbers never seen before in the past. The authors report in this paper a new approach of the ANN methodology using the time in its input instead of the previous 12 registered observations, as usually used. The authors intend to capture the time variation of the series along the years, and use this parameter as the input. The paper presents a comparison between the classic usage of the ANN methodology with a new modulation using the years and month in the input. The new modulation consists in four variations of the input of the ANN: A - just month; B - year and month; C - a combination of A and classic model and D - a combination of B and classic model. The models B and D improved the forecasting performance over the classic model, with a mean relative error of 5.98% and 5.79% in the test set, against the 6.36% for the classic model.

VIVO