Integrating Data Decomposition with Nonadditive Ensemble Learning for Air Passenger Demand Forecasting

Abstract

Accurately forecasting the demand for air passengers is vital for the aviation industry to formulate appropriate management strategies. Decomposition ensemble learning has attracted much attention from researchers of this problem because it is an effective way to improve forecasting accuracy. In contrast to common ways of generating ensemble forecasts, such as artificial intelligence and linear addition, our study employs the Choquet fuzzy integral. The Choquet integral is effective regardless of the training sample size and it uses a nonadditive fuzzy measure to explain the influence of the inputs on air passenger demand. Data on monthly air passenger flows from major airports in Taiwan were used to assess the effectiveness of the proposed decomposition ensemble models using the Choquet fuzzy integral to generate ensemble forecasts. The results in terms of level and directional forecasting accuracy showed that the proposed models— especially those that integrated smoothing (LOESS) (STL) and radial basis function network with the Choquet integral—significantly outperformed single (non-ensemble) forecasting models and the benchmark models considered.