Author: Wang, J.; Ge, P.; Liu, Z.
Title: Using Denoised LSTM Network for Tourist Arrivals Prediction Cord-id: h26i85pw Document date: 2021_1_1
ID: h26i85pw
Snippet: Precise tourist arrivals prediction is required since tourism products are perishable and vulnerable to environmental change. Many studies have been pursuing more effective techniques to forecast tourist arrivals after the worldwide COVID-19. A hybrid method based on singular spectrum analysis (SSA) and long short-term memory network (LSTM) that incorporates various varieties of time series, containing historical tourist arrivals and search intensity indices (SII), is proposed to make tourist ar
Document: Precise tourist arrivals prediction is required since tourism products are perishable and vulnerable to environmental change. Many studies have been pursuing more effective techniques to forecast tourist arrivals after the worldwide COVID-19. A hybrid method based on singular spectrum analysis (SSA) and long short-term memory network (LSTM) that incorporates various varieties of time series, containing historical tourist arrivals and search intensity indices (SII), is proposed to make tourist arrivals predictions. The proposed method is applied to the empirical studies and its results outperform all baseline models which verifies the effectiveness of the denoised deep learning method for high-frequency predictions. In addition, experimental results on independent SII variables reveal that SII data is of great significance to tourist arrivals predictions and provides practitioners with deeper comprehension of potential tourism forecasting factors. © 2021 IEEE.
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