Open AccessForecasting Currency in East Java: Classical Time Series vs. Machine Learning
Author(s) -
J A Putri,
Suhartono Suhartono,
Hendri Prabowo,
Novi Ajeng Salehah,
Dedy Dwi Prastyo,
Setiawan Setiawan
Publication year - 2021
Publication title -
indonesian journal of statistics and applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v5i2p284-303
Subject(s) - currency , inflow , java , artificial neural network , computer science , outflow , series (stratigraphy) , machine learning , artificial intelligence , time series , geography , economics , meteorology , paleontology , monetary economics , biology , programming language
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.