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- W4308824680 abstract "A short-term tourism demand forecasting method based on multivariate time series clustering and LSSVM is proposed. Firstly, continuous time samples are intercepted into multivariate time series samples by using sliding time window; Then, it uses the multivariate time series clustering method based on principal component analysis to classify them and generate similar time segment subsets; Finally, the LSSVM model is used to forecast according to the subset data of similar time periods. The results show that compared with the comparison model, the model can effectively improve the forecasting accuracy." @default.
- W4308824680 created "2022-11-16" @default.
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- W4308824680 creator A5061188546 @default.
- W4308824680 date "2022-10-03" @default.
- W4308824680 modified "2023-09-30" @default.
- W4308824680 title "Forecasting of Short-term Tourism Demand Based on Multivariate Time Series Clustering and LSSVM" @default.
- W4308824680 cites W2070036769 @default.
- W4308824680 doi "https://doi.org/10.1109/iaeac54830.2022.9929603" @default.
- W4308824680 hasPublicationYear "2022" @default.
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