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http://dx.doi.org/10.21289/KSIC.2021.24.3.323

A Comparison Study of Forecasting Time Series Models for the Harmful Gas Emission  

Jang, Moonsoo (Department of Statistics, Pusan National University)
Heo, Yoseob (Busan.Ulsan.Gyeongnam Branch, Korea Institute of Science and Technology Information(KISTI))
Chung, Hyunsang (Busan.Ulsan.Gyeongnam Branch, Korea Institute of Science and Technology Information(KISTI))
Park, Soyoung (Department of Statistics, Pusan National University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.24, no.3, 2021 , pp. 323-331 More about this Journal
Abstract
With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO2, NH3, H2S, CH4) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.
Keywords
Time-series forecasting; ARIMA; Random Forest; LSTM; Harmful gas emission;
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