Daily tourist flow forecasting using SPCA and CNN‐LSTM neural network-Reference-Cited by-同舟云学术

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Daily tourist flow forecasting using SPCA and CNN‐LSTM neural network

Published:2020-10-12 Issue:5 Volume:33 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Ni Tian¹^ORCID,Wang Lei¹²,Zhang Pengchao²,Wang Bin¹,Li Wei¹

Affiliation:

1. Department of Computer Science and Engineering Xi'an University of Technology Xi'an China

2. The Key Laboratory of Industrial Automation of Shaanxi Province Shaanxi University of Technology Hanzhong China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5980

Reference36 articles.

1. A EMD‐BP integrated model to forecast tourist number and applied to Jiuzhaigou;Shao‐Jiang L;J Intell Fuzzy Syst,2018

2. Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data

3. Seasonal SVR with FOA algorithm for single‐step and multi‐step ahead forecasting in monthly inbound tourist flow;Lijuan W;Knowl‐Based Syst,2016

4. Marketing international tourism to Australia

5. Multivariate exponential smoothing for forecasting tourist arrivals;Athanasopoulos G;J Travel Res,2012

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1. Machine learning applied to tourism: A systematic review;WIREs Data Mining and Knowledge Discovery;2024-07-04

2. Research on the Differentiation of Tourist Flow Network of Different Tourist Attractions in the Region;Applied Mathematics and Nonlinear Sciences;2023-12-27

3. Study on regional tourism performance evaluation based on the fuzzy analytic hierarchy process and radial basis function neural network;Annals of Operations Research;2023-04-12

4. A deep learning model based on multi-source data for daily tourist volume forecasting;Current Issues in Tourism;2023-03-09

5. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment;Open Geosciences;2023-01-01

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