Forecasting Currency in East Java: Classical Time Series vs. Machine Learning
DOI:
https://doi.org/10.29244/ijsa.v5i2p284-303Keywords:
arimax, arimax-dnn, deep neural network, currency, forecastingAbstract
Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.
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References
[BI], Bank Indonesia. (2018). Regional Economic and Financial Studies of East Java Province. Surabaya: Bank Indonesia Representative Office, East Java Province.
Crone, S. F., & Kourentzes, N. (2009). Input-variable specification for neural networks-an analysis of forecasting low and high time series frequency. 2009 International Joint Conference on Neural Networks, 619–626. IEEE.
Dordonnat, V., Koopman, S. J., Ooms, M., Dessertaine, A., & Collet, J. (2008). An hourly periodic state space model for modelling French national electricity load. International Journal of Forecasting, 24(4): 566–587.
Faraway, J., & Chatfield, C. (1998). Time series forecasting with neural networks: a comparative study using the air line data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(2): 231–250.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4): 679–688.
Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4): 111–122.
Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1): 479–489.
Lee, M. H., Hamzah, N., & others. (2010). Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. Proceedings of the Regional Conference on Statistical Sciences, 349–361.
Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4): 527–529.
Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16(4): 451–476.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018a). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS One, 13(3): e0194889.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018b). The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 34(4): 802–808.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61: 85–117.
Shumway, R. H., & Stoffer, D. S. (2006). Time Series Analysis and Its Applications with R Examples. Pittsburg: Springer.
Suhartono, D. E. A., Prastyo, D. D., Kuswanto, H., & Lee, M. H. (2019). Deep Neural Network for Forecasting Inflow and Outflow in Indonesia. Sains Malaysiana, 48(8): 1787–1798.
Suhartono, Lee, M. H., & Prastyo, D. D. (2015). Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects. AIP Conference Proceedings, 1691(1), 050026. AIP Publishing LLC.
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159–175.
Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2): 501–514.
