A novel hybrid deep learning approachfor tourism demand forecasting

This paper proposes a new hybrid deep learning framework that combines search query data, autoencoders (AE) and stacked long-short term memory (staked LSTM) to enhance the accuracy of tourism demand prediction. We use data from Google Trends as an additional variable with monthly tourist arrivals Marrakech, Morocco. The AE is applied feature extraction procedure dimension reduction, extract valuable information mine nonlinear incorporated in data. extracted features are fed into LSTM predict arrivals. Experiments carried out analyze performance forecast results proposed method compared individual models, different principal component analysis (PCA) based models. experimental show outperforms other