Implementasi Sliding Window Algotihm pada Prediksi Kurs berbasis Neural Network

Primandani Arsi, Tri Astuti, Desty Rahmawati, Pungkas Subarkah

Abstract


Time series is sequential data based on time sequence. Time series data can be used for prediction topics, one of the prediction topics that is always interesting to study is exchange rate prediction. In the case of exchange rate prediction, an appropriate data preprocessing stage is required. The success of this preprocessing stage will have a major effect on the resulting RMSE value. There is an important technique in determining the best RMSE value, especially in time series data, one of which is the windowing technique. The windowing technique is the stage of transforming time series data into cross sectional. Window size has an important role in time series data. However, there is no standard in window size. The Window size experiment starts with a small value and then increases to a larger value until it reaches a certain point with the best RMSE. In this research, an experiment will be conducted on windows size on exchange rate data based on a neural network. The purpose of this research is to optimize the RMSE of a data mining model based on windows parameters. The implementation of sliding windows is carried out in the scenarios of window sizes 4, 6, and 28. Based on the experiments conducted, the best RMSE is on windows size 6 = 0.014 +/- 0.000. With a combination of neural network parameters in the form of training cycles = 1000, learning rate = 0.1 and momentum = 0.1.


Keywords


Sliding window; Window size; exchange rate; Neural network; RMSE

Full Text:

PDF

References


Arsi, P., & Prayogi, J. (2020). Optimasi Prediksi NilaiTukar Rupiah Terhadap Dolar Menggunakan Neural Network Berbasiskan Algoritma Genetika. Jurnal Informatika, 7(1), 8–14. https://doi.org/10.31311/ji.v7i1.6793

Aulia, A. A., Elhanafi, A. M., Dafitri, H., Aulia, A., Elhanafi, A. M., & Dafitri, H. (2021). Implementasi Algoritma Gated Recurrent Unit Dalam Melakukan Prediksi Harga Kelapa Sawit Dengan Memanfaatkan Model Recurrent Neural Network ( RNN ). Prosiding SNASTIKOM: Seminar Nasional Teknologi Informasi & Komunikasi Paper, 288–294.

Ćalasan, M., Abdel Aleem, S. H. E., & Zobaa, A. F. (2020). On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Conversion and Management, 210(January), 112716. https://doi.org/10.1016/j.enconman.2020.112716

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623

Ding, Z., & Fei, M. (2013). An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 3). IFAC. https://doi.org/10.3182/20130902-3-CN-3020.00044

Dwi Kartini, Friska Abadi, & Triando Hamonangan Saragih. (2021). Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 39–44. https://doi.org/10.29207/resti.v5i1.2602

Gao, Q., Xu, H., & Li, A. (2022). The analysis of commodity demand predication in supply chain network based on particle swarm optimization algorithm. Journal of Computational and Applied Mathematics, 400, 113760. https://doi.org/10.1016/j.cam.2021.113760

Hodson, T. O. (2022). Root Mean Square Error (RMSE) or Mean Absolute Error (MAE): when to use them o not. Geoscientific Model Development, 7(March), 1525–1534.

Indonesia, B. (n.d.). Data Kurs Rupiah - Dolar. Retrieved from https://www.bi.go.id/

Kulanuwat, L., Chantrapornchai, C., Maleewong, M., Wongchaisuwat, P., Wimala, S., Sarinnapakorn, K., & Boonya-Aroonnet, S. (2021). Anomaly detection using a sliding window technique and data imputation with machine learning for hydrological time series. Water (Switzerland), 13(13). https://doi.org/10.3390/w13131862

Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89–93. https://doi.org/10.1016/j.jefas.2016.07.002

Norwawi, N. M. (2021). Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia. Data Science for COVID-19 Volume 1: Computational Perspectives. Elsevier Inc. https://doi.org/10.1016/B978-0-12-824536-1.00025-3

Panda, M. M., Panda, S. N., & Pattnaik, P. K. (2022). Multi currency exchange rate prediction using convolutional neural network. In Materials Today: Proceedings. Elsevier Ltd. https://doi.org/10.1016/j.matpr.2020.11.317

Pérez-Chacón, R., Asencio-Cortés, G., Martínez-Álvarez, F., & Troncoso, A. (2020). Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand. Inf. Sci. (Ny), 540, 160–174.

Ranjan, K. G., Tripathy, D. S., Prusty, B. R., & Jena, D. (2021). An improved sliding window prediction-based outlier detection and correction for volatile time-series. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(1), 1–13. https://doi.org/10.1002/jnm.2816

Somantri, O., Purwaningrum, S., Informatika, J. T., Cilacap, P. N., Studi, P., & Informatika, M. (2022). Model Support Vector Machine ( Svm ) Berdasarkan Parameter, 8, 17–24.

Tomar, D., Tomar, P., Bhardwaj, A., & Sinha, G. R. (2022). Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/7216959

Wahyuni, R. E. (2021). Optimasi Prediksi Inflasi Dengan Neural Network Pada Tahap Windowing Adakah Pengaruh Perbedaan Window Size? Technologia: Jurnal Ilmiah, 12(3), 176. https://doi.org/10.31602/tji.v12i3.5181


Article Metrics

Abstract has been read : 466 times
PDF file viewed/downloaded: 0 times


DOI: http://doi.org/10.25273/doubleclick.v6i1.13496

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 DoubleClick: Journal of Computer and Information Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indexed By.

 

   

   

 

Kantor Sekertariat:
Program Studi Informatika, Fakultas Teknik
Universitas PGRI Madiun
Jl. Auri No. 14-16  Kota Madiun 63118
E-mail :  doubleclick@unipma.ac.id
 
 

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.

View My Stats