Forecasting Air Travel Demand

6   A novel seasonal decomposition-based short-term forecasting framework with Google Trends data

For monthly and quarterly historical data, seasonality is one of the most significant data patterns. When predicting the future demand, it is essential to measure and adjust the seasonality in order to understand the underlying historical trends more precisely. However, it is difficult for the existing seasonality analysis procedures to precisely deal with the seasonality adjustment on every economic time series, due to the individual characteristics of every time series, especially for the moving holiday effects. Therefore, in the short-term air travel demand forecasting, we usually encounter a poor forecasting performance problem for those months ...