본문 바로가기
  • Home

Application of Informer for time-series NO2 prediction

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(7), pp.11-18
  • DOI : 10.9708/jksci.2023.28.07.011
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : June 8, 2023
  • Accepted : July 20, 2023
  • Published : July 31, 2023

Hye Yeon Sin 1 Minchul Kang 2 Joonsung Kang 3

1덕성여자대학교
2차의과학대학교
3강릉원주대학교

Accredited

ABSTRACT

In this paper, we evaluate deep learning time series forecasting models. Recent studies show that those models perform better than the traditional prediction model such as ARIMA. Among them, recurrent neural networks to store previous information in the hidden layer are one of the prediction models. In order to solve the gradient vanishing problem in the network, LSTM is used with small memory inside the recurrent neural network along with BI-LSTM in which the hidden layer is added in the reverse direction of the data flow. In this paper, we compared the performance of Informer by comparing with other models (LSTM, BI-LSTM, and Transformer) for real Nitrogen dioxide (NO2) data. In order to evaluate the accuracy of each method, mean square root error and mean absolute error between the real value and the predicted value were obtained . Consequently, Informer has improved prediction accuracy compared with other methods.

Citation status

* References for papers published after 2022 are currently being built.