발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.6] MPC-TimesNet: Time-Series Long-Term Forecasting Air Pollution with Multiple Periodicity Consideration Model |
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| 논문투고자 |
Jean Ho Kim, Hyuk Jae Kwon, Ha Young Kim |
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| 논문내용 |
Urban air pollution has a significant impact on environment, public health, social economic, and policydecision. In accordance with increasing global necessity of constant monitoring and accurate forecastingof air pollution among governments and institutions, research into applying transformer-based models topredict pollutants preceded. However, the models have been limited in their ability to thoroughlyconsider local information within periodicity, which is one of the key inherent patterns in pollution data.To address the limitation, this paper proposes MPC(Multiple Periodicity Consideration)-TimesNet modeldesigned to handle intricate locality information within periodicity by considering not only lowfrequencies information, but also that of high frequencies. The proposed model added HFEM(HighFrequency Extraction Module) to the previous TimesNet model in order to exquisitely account for highfrequencies data. In addition, we lowered the size of representations as the self-information gets smallerto address noise issues. To evaluate our model, we collected data of five air pollutants (NO2, OZ, CO2,SO2, PM10) among three regions―Songpa-gu, Youngdeungpo-gu, Jung-gu―in Seoul. As a result of theexperiment, the proposed model recorded 0.3615, 0.3277, 0.3893 in mean square error and 0.3764,0.3864, 0.39 in mean absolute error, respectively. Our model outperforms twelve previous SOTA modelsincluding baseline in all regions. In conclusion, we verified that summating HFEM increased theeffectiveness of the model in forecasting air pollution by comparing their performance to the baseline,TimesNet. |
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