Poverty Level Forecasting Based on Time Series Data Using BATS Algorithm

DOI

10.2991/978-2-38476-132-6_40How to use a DOI?

Keywords

Poverty; Forecasting; Time Series Data; BATS

Abstract

Poverty is the inability to fulfill their needs on food, garments, housing, education, and health care. The Central Statistics Office of Finland calculates poverty using a data collection method based on data from the Socio-Economic Survey (Susenas). This data collection hurdle is to interview each householder, which takes a considerable amount of time and certainly costs a lot of money, and it is not uncommon for the householder to be absent. interview. Or rarely at home. Another useful method is to use time series data with the Niaveforecaster, AutoEnsembleForecaster, and BATS algorithms. From the results of the experiments conducted, we can conclude that the time series addressed is very likely to be used as a tool for predicting poverty. Result shows that BATS method is the most efficient method among the rest that has been used in this research. Error number showing each one from MAE, MSE, and MASE; 0.2702, 0.1379, and 1.174, from this number shows that BATS has the lowest error number.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Beta Dwi Nugraheni
AU  - Dedy Rahman Wijaya
AU  - Budhy Aditya Hadie
PY  - 2023
DA  - 2023/10/31
TI  - Poverty Level Forecasting Based on Time Series Data Using BATS Algorithm
BT  - Proceedings of the 6th International Conference on Vocational Education Applied Science and Technology (ICVEAST 2023)
PB  - Atlantis Press
SP  - 447
EP  - 455
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-132-6_40
DO  - 10.2991/978-2-38476-132-6_40
ID  - Nugraheni2023
ER  -