Multivariate Time Series Data Forecasting Using Multi-Output NARNN Model
- 10.2991/assehr.k.210305.041How to use a DOI?
- Multivariate Time Series Forecasting, Logistic Function, Resilient Backpropagation Learning, Multi-Output NARNN Model
This research proposes the multi-output Nonlinear Autoregressive Neural Network (NARNN) method to forecast multivariate time series data containing the input layer, one hidden layer, and the output layer. The multi-output NARNN method is performed by applying the logistic activation function and the resilient backpropagation learning algorithm. The stage of determining the input variable is chosen based on the number of data frequencies. The number of neurons in the hidden layer is half of the number of input variables. Simulation and empirical studies are conducted to test whether the proposed method works well for multivariate time series data forecasting. The simulation results show that the best performance is the simulation data generated from the MESTAR nonlinear model. The simulation study results are as expected. Empirical studies on Indonesia’s inflation and Bank Indonesia interest rate data show that the multi-output NARNN method provides better forecasting accuracy than the VAR, VMA, and VARMA methods with a total MSE value of 0.054655 and a total MAPE of 0.026853 in the testing data.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hermansah AU - Dedi Rosadi AU - Abdurakhman AU - Herni Utami AU - Gumgum Darmawan PY - 2021 DA - 2021/03/08 TI - Multivariate Time Series Data Forecasting Using Multi-Output NARNN Model BT - Proceedings of the 7th International Conference on Research, Implementation, and Education of Mathematics and Sciences (ICRIEMS 2020) PB - Atlantis Press SP - 288 EP - 294 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.210305.041 DO - 10.2991/assehr.k.210305.041 ID - 2021 ER -