Poverty Level Prediction Based on Time Series Data using Auto Arima

Open Access

Issue		BIO Web Conf. Volume 144, 2024 1^st International Graduate Conference on Smart Agriculture and Green Renewable Energy (SAGE-Grace 2024)


Article Number		04008
Number of page(s)		9
Section		Socioeconomic and Community Development
DOI		https://doi.org/10.1051/bioconf/202414404008
Published online		25 November 2024

Akpan, A. I., & Zikos, D. (2023). Rural Agriculture and Poverty Trap: Can Climate- Smart Innovations Provide Breakeven Solutions to Smallholder Farmers? Environments, 10 (4), 57. https://doi.org/ 10.3390/environments10040057 [CrossRef] [Google Scholar]
Vu Tuan Anh. 1997. “Criteria for Rural Poverty Monitoring,” Vietnam’s Socio- Economic Development, Spring. [Google Scholar]
E. R. Alfiyyah, R. Andreswari, and E. Sutoyo, “ANALYSIS AND DETECTION OF FRAUD ON CALL DATA USING K NEAREST NEIGHBOR ALGORITHM (CASE STUDY: PT XYZ).” [Google Scholar]
J. Blumenstock, G. Cadamuro, and R. On, “Predicting poverty and wealth from mobile phone metadata.” [Online]. Available: www.sciencemag.org [Google Scholar]
B. Babenko, J. Hersh, D. Newhouse, A. Ramakrishnan, and T. Swartz, “Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico,” Nov. 2017, [Online]. Available: http://arxiv.org/abs/1711.06323 [Google Scholar]
A. Pangestu, R. Dedy, S. T. Wijaya, E. Hernawati, and M. Kom, “APLIKASI PENGOLAHAN DATA PREDIKSI KEMISKINAN BERBASIS E-COMMERCE MENGGUNAKAN DECISION TREE DAN WRAPPER FEATURE SELECTION APPLICATION BASED OF E-COMMERCE POVERTY PREDICTION DATA PROCESSING DECISION TREE AND WRAPPER FEATURE SELECTION.” [Google Scholar]
T. F. Aulia, D. R. Wijaya, E. Hernawati, and W. Hidayat, “Poverty Level Prediction Based on E-Commerce Data Using K-Nearest Neighbor and Information-Theoretical- Based Feature Selection,” in 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 28–33. doi: 10.1109/ICOIACT50329.2020.9332083. [Google Scholar]
D. R. Wijaya, N. L. P. S. P. Paramita, A. Uluwiyah, M. Rheza, A. Zahara, and D. R. Puspita, “Estimating city-level poverty rate based on e-commerce data with machine learning,” Electronic Commerce Research, vol. 22, no. 1, pp. 195–221, Mar. 2022, doi: 10.1007/s10660-020-09424-1. [CrossRef] [Google Scholar]
A. Pangestu, E. Hernawati, D. Rahman Wijaya, and W. Hidayat, “Wrapper Feature Selection for Poverty Level Prediction Based on E-Commerce Dataset,” 2020. [Google Scholar]
SKTime, “Auto ARIMA,” 2013. https://www.sktime.org/en/stable/api_reference/auto_generated/sktime.forecasting.ari ma.AutoARIMA.html [Google Scholar]
Zheng, B. 1993. “An axiomatic characterization of the Watts poverty index,” Economics Letters, 42 (1): 81-86. [CrossRef] [Google Scholar]
I. Fitria, M. Sayekti Kuncaraning Alam, P. Studi Matematika, and I. Teknologi Kalimantan, “Perbandingan Metode ARIMA dan Exponential Smoothing pada Peramalan Harga Saham LQ45 Tiga Perusahaan dengan Nilai Earning Per Share (EPS) Tertinggi.” [Google Scholar]
Liu H, Li C, Shao Y, Zhang X, Zhai Z et al. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011-2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models. J Infect Public Health 2020; 13:287–294 [CrossRef] [PubMed] [Google Scholar]
S.M. Bartolomei et al. A note on a comparison of exponential smoothing methods for forecasting seasonal series. International Journal of Forecasting [Google Scholar]
BROWN, R. G., MEYER, R. F., The fundamental theory of exponential smoothing, Operations Research, 9, 1961, p. 673-685 [CrossRef] [Google Scholar]
MONTGOMERY, D. C., JOHNSON, L. A., GARDINER, J. S., Forecasting and Time Series Analysis, McGraw-Hill, Inc., 1990, ISBN 0-07-042858-1 [Google Scholar]
Christensen, S.L., Anglov, J.T.B., Christensen, J.M., Olsen, E. and Poulsen, O.M. 1993. Application of a new AMIQAS computer program for integrated quality control, method evaluation and proficiency testing. Fresenius J. Anal. Chem, 345 : 343 –350 [CrossRef] [Google Scholar]
J.D. Amato et al. The real time predictive content of money for output. Journal of Monetary Economics [Google Scholar]
Yang S, Chen HC, Chen WC, Yang CH. Student enrollment and teacher statistics forecasting based on time-series analysis. Comput Intell Neurosci 2020; 2020:1246920 [PubMed] [Google Scholar]

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