Conference Paper2024

Zero-Shot Inference with TimeGPT for Air Quality Prediction in Bangladesh

J. M. Sadik-Ul Islam Smaron, M. M. Simoon, M D. Saroar Jaman, Jahanggir Hossain Setu, Ashraful Islam, M Ashraful Amin

2024 27th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 2534-2539

DOI: 10.1109/iccit64611.2024.11022097URL

Abstract

Air quality represents an important environmental challenge, particularly in densely populated urban areas, where the presence of pollutants like Particulate Matter (PM) considerably threatens public health. This study aims to develop predictive models for PM2.5 concentrations utilizing data sourced from the U.S. Embassy air quality monitor spanning from January 1, 2018, to September 17, 2024. A robust methodology was employed, encompassing two Deep Learning (DL) models, Long Short-Term Memory (LSTM) and TimeGPT. The results demonstrate that TimeGPT consistently outperforms LSTM across all key performance metrics. The average Mean Absolute Error (MAE) for TimeGPT was determined to be 10.76, which is notably lower than the average MAE of 15.89 observed for LSTM. Furthermore, TimeGPT exhibited an average Mean Absolute Percentage Error (MAPE) of 4.68, in contrast to LSTM’s average MAPE of 9.84. The Root Mean Squared Error (RMSE) values corroborated this trend, with TimeGPT yielding an RMSE of 13.58, compared to 17.76 for LSTM. TimeGPT achieved its optimal MAE of 3.81 when it was trained using data from 2018 to 2024. These results emphasize the prospects of modern Machine Learning (ML) methodologies in the accurate forecasting of air quality.

Keywords

  • air quality forecasting
  • PM2.5 forecasting
  • particulate matter
  • TimeGPT
  • zero-shot inference
  • LSTM
  • time series forecasting
  • Bangladesh
  • urban air pollution

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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