A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization

Cyuan Heng Luo, Hsuan Yang, Li Pang Huang, Sachit Mahajan, Ling Jyh Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The problem of air pollution has become a serious issue in developed as well as developing countries. Unfortunately, most of the current solutions are not very effective and this makes it important to have an efficient early warning system for monitoring and forecasting air quality. Our main focus is to build a real-time forecasting system with high accuracy, and deploy it in Taiwan. In this paper, we propose a forecast method called Adaptive Iterative Forecast (AIF), which can predict the value of PM2.5 for the next few hours (by linear programming, normalization and time-series) based on the trend of historical data. The goal of this research is to develop an efficient and accurate forecast model. Through various comparative analyses, we have proved that our model can achieve significant results. Based on the results, we have also built a real-time forecasting system which allows the users to stay aware of the air quality and plan their day to day life.

Original languageEnglish
Title of host publicationProceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages78-81
Number of pages4
ISBN (Electronic)9781728112299
DOIs
Publication statusPublished - 2018 Dec 24
Event2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 - Taichung, Taiwan
Duration: 2018 Nov 302018 Dec 2

Publication series

NameProceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018

Conference

Conference2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
CountryTaiwan
CityTaichung
Period18/11/3018/12/2

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Keywords

  • Forecasting
  • Linear programming
  • Normalization
  • PM2.5
  • Time series

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

Cite this

Luo, C. H., Yang, H., Huang, L. P., Mahajan, S., & Chen, L. J. (2018). A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization. In Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 (pp. 78-81). [8588482] (Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2018.00026