Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning
DOI:
https://doi.org/10.30812/matrik.v24i1.4052Keywords:
Currency Circulation, Forecasting, Hybrid Model, Long Short Term Memory, ProphetAbstract
The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibiting
trend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
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Short-Term Currency Trends,†Scientific Bulletin of Mukachevo State University Series “Economicsâ€, vol. 7, no. 2, pp. 75–86,
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[22] Z. Chen, C. Wang, L. Lv, L. Fan, S. Wen, and Z. Xiang, “Research on Peak Load Prediction of Distribution Network Lines
Based on Prophet-LSTM Model,†Sustainability, vol. 15, no. 15, p. 11667, jul 2023, https://doi.org/10.3390/su151511667.
[23] S. Arslan, “A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series
data,†PeerJ Computer Science, vol. 8, p. e1001, jun 2022, https://doi.org/10.7717/peerj-cs.1001.
[24] V. Nur Aziza, F. H. Moh’d, F. A. Maghfiroh, K. A. Notodiputro, and Y. Angraini, “Performance Comparison
of Sarima Intervention and Prophet Models for Forecasting the Number of Airline Passenger at Soekarno-Hatta
International Airport,†BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 4, pp. 2107–2120, dec 2023,
https://doi.org/10.30598/barekengvol17iss4pp2107-2120.
[25] Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, “Time-series forecasting of seasonal items sales using machine learning – A
comparative analysis,†International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100058, apr 2022,
https://doi.org/10.1016/j.jjimei.2022.100058.
[26] M. Ula, V. Ilhadi, and Z. M. Sidek, “Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price
Forecasting,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 259–272, jan
2024, https://doi.org/10.30812/matrik.v23i2.3267.
[27] I. N. G. A. Astawa, I. P. B. A. Pradnyana, and I. K. Suwintana, “Comparison of RNN, LSTM, and GRU Methods on
Forecasting Website Visitors,†Journal of Computer Science and Technology Studies, vol. 4, no. 2, pp. 11–18, aug 2022,
https://doi.org/10.32996/jcsts.2022.4.2.3.
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[29] K. Chowdhury, “10 Hyperparameters to keep an eye on for your LSTM model— and other tips.â€
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[31] L. Zhoul, M. Chenl, and Q. Ni, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), https://doi.org/10.1109/
SSCI47803.2020.9308543, isbn = 978-1-7281-2547-3, mendeley-groups = [MATRIK Vol 24 No 1] Vivin Nur Aziza, month
= dec, pages = 595–601, publisher = IEEE, title = A hybrid Prophet-LSTM Model for Prediction of Air Quality Index, url =
https://ieeexplore.ieee.org/document/9308543/, year = 2020.
[32] R. N. M. Sanusi, B. Susetyo, and U. D. Syafitri, “Simulation of the Sarima Model With Three-Way Anova and Its Application
in Forecasting Large Chillies Prices in Five Provinces on Java Island,†BAREKENG: Jurnal Ilmu Matematika dan Terapan,
vol. 17, no. 1, pp. 0253–0262, apr 2023, https://doi.org/10.30598/barekengvol17iss1pp0253-0262.
[33] M. D. Kartikasari, “Forecasting of Currency Circulation in Indonesia Using Hybrid Extreme Learning Machine,â€
BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 2, pp. 635–642, jun 2022, https://doi.org/10.30598/
barekengvol16iss2pp635-642.
[34] O. Ozdemir and C. Yozgatligil, “Forecasting performance of machine learning, time series, and hybrid methods for low- and
high-frequency time series,†Statistica Neerlandica, vol. 78, no. 2, pp. 441–474, may 2024, https://doi.org/10.1111/stan.12326.
[35] J. A. Putri, S. Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and S. Setiawan, “Forecasting Currency in East Java:
Classical Time Series vs. Machine Learning,†Indonesian Journal of Statistics and Its Applications, vol. 5, no. 2, pp. 284–303,
jun 2021, https://doi.org/10.29244/ijsa.v5i2p284-303.
[36] M. Monica, A. Suharsono, B. W. Otok, and A. Wibisono, “Hybrid SARIMA-FFNN model in forecasting
cash outflow and inflow,†Journal of Physics: Conference Series, vol. 2106, no. 1, p. 012002, nov 2021,
https://doi.org/10.1088/1742-6596/2106/1/012002.
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2024-11-06
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[1]
V. N. Aziza, U. D. Syafitri, and A. Fitrianto, “Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning”, MATRIK, vol. 24, no. 1, pp. 73–84, Nov. 2024, doi: 10.30812/matrik.v24i1.4052.
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