Abstract
In this paper, we proposed an improved a time series forecasting method using time series decomposition and a deep learning model. The proposed method combined Seasonal-Trend decomposition using Loess (STL) and Gated Recurrent Units (GRU) architecture to forecast time series data. We used trend, seasonality and the remainder as input in GRU model simultaneously. In proposed model, it does not assume independence between the components differently from other papers. According to the experiments for several data in various fields, our model outperforms other traditional methods such as Seasonal ARIMA, Holt-Winters and GRU without decomposition. Furthermore, we also demonstrated that the proposed model decrease MSE comparing with the GRU model assuming independence.
H. J. Jo, W. J. Kim and H. K. Goh—Equal contribution.
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Jo, H.J., Kim, W.J., Goh, H.K., Jun, CH. (2021). An Improved Time-Series Forecasting Model Using Time Series Decomposition and GRU Architecture. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_68
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