Abstract—Deep learning concept is popularly used to solve a
classification problem including the model identification in
time series analysis. For the time series models such as the
autoregressive integrated moving average (ARIMA) model and
the seasonal autoregressive integrated moving average
(SARIMA) model, statisticians mostly identify the
ARIMA/SARIMA orders before building the forecasting
model. To identify them, they investigate sample ACF and
PACF to extract p, d, q while most researchers automate this
step using the likelihood based-method via AIC ignoring the
residual diagnostic. This paper uses ACF, PACF and
differencing time series images as inputs to the convolutional
neural network architecture that automatically identifies the
ARIMA/SARIMA orders, called the automatic ARIMA order
identification convolutional neural network (AOC). The
performance of AOC outperforms the likelihood based-method
in terms of identifying ARIMA order via precision, recall and
f1-score. Moreover, AOC is extended to identify the SARIMA
order, called the automatic SARIMA order identification
convolutional neural network (ASOC). The performance of the
ASOC model provides better performance than the likelihood
method via precision, recall and f1-score.
Index Terms—Convolutional neural network, ARIMA, SARIMA, ACF, PACF.
The authors are with the Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Paisit Khanarsa and Krung Sinapiromsaran, "Automatic SARIMA Order Identification Convolutional Neural Network," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 662-668, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).