Regression Models for Time Series with Increasing Seasonality

In this paper, three regression models are compared according to their performance in terms of forecast accuracy, for the case of time series with increasing seasonality. 617 series are used in the comparison as well as three models, being one of them an original contribution of this work. In addition, the regression models are compared with the autoregressive approach, commonly used in the forecast of these series. The results indicate that the performance of the regression models depends on the forecast horizon and on the degree of curvature of the series. At fewer curvature and longer forecast horizon, its performance is better. The conditions under which the regression models outperform the autoregressive approach are discussed. Also, the performance of the prediction intervals in order to improve its effectiveness is analyzed.