Cited By
View all- Pu THuang CYang JHuang M(2023)Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration PredictionSustainability10.3390/su15131058415:13(10584)Online publication date: 5-Jul-2023
Forecasting international migrants using grey model with heat label
Pages 652 - 656
Abstract
Migration is an important social phenomenon in the development of human society. Driven by economy, population, geography, policy, and other factors, accurate prediction of migration has always been very difficult. The grey model has the advantages of small sample size, easy calculation, no regularity in sample size, and good prediction precision, so it is very suitable for the prediction of international migration. Based on the correlation and cumulative effect of data sequence, this paper optimizes the initial value conditions of the grey model, and proposes the grey model of heat label. The proposed model is applied to the prediction of international migration from 1970 to 2020, and compared with the traditional grey model and other models, computation results show the model is practical and effective, and has positive theoretical and practical significance for international migration prediction.
References
[1]
International Organization for Migration. (2022). World migration report (WMR) 2022. https://worldmigrationreport.iom.int/
[2]
Bijak, J. 2011. Forecasting Migration: Selected Models and Methods. In: Forecasting International Migration in Europe: A Bayesian View. The Springer Series on Demographic Methods and Population Analysis, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8897-0_4
[3]
Buettner, Thomas, and Rainer Muenz. 2020. Migration Projections: The Economic Case. KNOMAD Paper No. 37, World Bank, Washington, DC.
[4]
Anjos C, Campos P. 2010. The role of social networks in the projection of international migration flows: An agent-based approach. Work session on demographic projections, 305.
[5]
COHEN, JE, ROIG, M, REUMAN, DC, 2008. International migration beyond gravity: A statistical model for use in population projections. Proceedings of the National Academy of Sciences of the United States of America, 105(40):15269-15274.
[6]
Cappelen Å, Skjerpen T, Tønnessen M. 2015. Forecasting immigration in official population projections using an econometric model. International Migration Review, 49(4): 945-980. https://doi.org/10.1111/imre.120
[7]
Bijak J. 2010. Forecasting international migration in Europe: A Bayesian view. Springer Science & Business Media.
[8]
Azose J J, Raftery A E. 2015. Bayesian probabilistic projection of international migration. Demography, 52(5): 1627-1650.
[9]
Caleb Robinson and Bistra Dilkina. 2018. A Machine Learning Approach to Modeling Human Migration. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS '18). Association for Computing Machinery, New York, NY, USA, Article 30, 1–8. https://doi.org/10.1145/3209811.3209868
[10]
Alexandr A. Tarasyev, Gavriil A. Agarkov, and Seyed Iman Hosseini.2018. Machine learning in labor migration prediction. In AIP Conference Proceedings 1978, 440004. https://doi.org/10.1063/1.5044033
[11]
Golenvaux N, Alvarez P G, Kiossou H S, 2020. An LSTM approach to Forecast Migration using Google Trends. arXiv preprint arXiv:2005.09902, 2020.
[12]
Zhang, L., Luo, L., Hu, L., & Sun, M. 2020. An SVM-Based Classification Model for Migration Prediction of Beijing. Engineering Letters, 28(4).
[13]
Azizi, S. and Yektansani, K. 2020. Artificial Intelligence and Predicting Illegal Immigration to the USA. Int Migr, 58: 183-193. https://doi.org/10.1111/imig.12695
[14]
Hussain, N. H. M. 2021. Machine learning of the reverse migration models for population prediction: a review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 1830-1838. https://doi.org/10.17762/turcomat.v12i5.2197
[15]
J. L. Deng. 1989. Introduction to Grey system theory. J. Grey Syst. 1, 1 (1989), 1–24.
[16]
Wu, Lifeng, 2013.The effect of sample size on the grey system model. Applied Mathematical Modelling, vol.37, pp.6577-6583. https://doi.org/10.1016/j.apm.2013.01.018.
Index Terms
- Forecasting international migrants using grey model with heat label
Index terms have been assigned to the content through auto-classification.
Recommendations
Forecasting the turning time of stock market based on Markov-Fourier grey model
This paper presents an integration prediction method including grey model (GM), Fourier series, and Markov state transition, known as Markov-Fourier grey model (MFGM), to predict the turning time of Taiwan weighted stock index (TAIEX) for increasing the ...
Applying fuzzy grey modification model on inflow forecasting
This paper investigates a modified grey model for forecasting the inflow of a reservoir. The integral form of the background value is employed for the original grey model, GM(1,1), to improve accuracy and applicability. Thereafter, the Fourier series is ...
The growth forecast in the number of broadband services subscribers using grey model
CEA'07: Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and ApplicationsConsidering the need to prepare short to mid-term plan and budget for building and maintaining costly infrastructure for the fast growing broadband services in Taiwan, this paper attempted to forecast the growth in the number of broadband subscribers in ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2022
753 pages
ISBN:9781450397780
DOI:10.1145/3569966
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 December 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- National Natural Science Foundation of China
Conference
CSSE 2022
CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering
October 21 - 23, 2022
Guilin, China
Acceptance Rates
Overall Acceptance Rate 33 of 74 submissions, 45%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 13Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)2
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
Cited By
View all- Pu THuang CYang JHuang M(2023)Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration PredictionSustainability10.3390/su15131058415:13(10584)Online publication date: 5-Jul-2023
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format