Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting

Abstract View: 165, PDF Download: 186

Authors

  • Mega Silfiani Institut Teknologi Kalimantan https://orcid.org/0000-0002-2290-6351
  • Farida Nur Hayati Institut Teknologi Kalimantan
  • Muhammad Azka Institut Teknologi Kalimantan

DOI:

https://doi.org/10.32665/statkom.v2i1.1594

Keywords:

Double SARIMA, Forecasting, MASE, MdAPE, Stock

Abstract

Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns.

Objective: This study aims to identify the most accurate model for stock forecasting.

Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202.  Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy.

Results: Following conducting each model, we assessed that the best models are DSARIMAX (0,1,[4]) ([3],1,1)5(1,1,0)253, regarding MASE and MdAPE corresponding to approximately 1.423 and 0.111. The scope of this study has limitations to a test set for one-month forecast periods.

Conclusion: As stock prices rise, investors require precise forecasts. Models of forecasting must perform well. This analysis shows how the DSARIMA generate forecasts stock prices more accurately. This investigation evaluated the closing stock price of UNVR. Both MASE and MdAPE assess prediction. After analyzing each model, DSARIMAX (0,1,[4])([3],1,1)5(1,1,0)253 has the lowest MASE and MdAPE values, 1.423 and 0.111, respectively. The procedure lasted one month. Research may combine forecasts and improve their accuracy.

 

References

Bowerman, B. L., O’Connell, R.T.O., Koehler, A. B. (2005). “Forecasting, Time series, and Regression: an Applied Approach 4th ed”, USA: Brook/Cole, Thomson Learning, Inc.

Dinata, S.A.W., Azka, M., Faisal, M., Suhartono, Yendra, R. and Gamal, M. D. H. (2020). “Short-Term load forecasting double seasonal ARIMA methods: An evaluation based on Mahakam-East Kalimantan data”, AIP Conference Proceedings 2268, 020004, https://doi.org/10.1063/5.0017643.

Gupta, A., and Kumar, A. (2022). “Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models,” Journal of Hydro-environment Research, 45, 39-52, https://doi.org/10.1016/j.jher.2022.10.002.

Hayati, F. N. and Ulama, B. S. S. (2016) “Peramalan Harga Saham Jakarta Islamic Index Menggunakan Metode Vector Autoregressive,” Jurnal Sains dan Seni ITS, 2337-3520. http://dx.doi.org/10.12962/j23373520.v5i2.16931

Handani, S. and Astawinetu, E. D. (2020). Teori Portofolio dan Pasar Modal Indonesia, Scopindo Media Pustaka.

Hyndman, R.J. and Koehler, A.B. (2006) “Another look at measures of forecast accuracy”, International Journal of Forecasting, 22 (4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001

Khoiriyah, N., and Cahyani, N. (2022). “Peramalan Banyaknya Pasien Rawat Jalan dengan Menggunakan Metode Brown's Double Exponential Smoothing,” Jurnal Statistika Dan Komputasi , 1(1), 23-30, https://doi.org/10.32665/statkom.v1i1.451.

Li, Y., Wu, K., and Liu, J. (2023). “Self-paced ARIMA for robust time series prediction,” Knowledge-Based Systems, 269, 110489, https://doi.org/10.1016/j.knosys.2023.110489.

Mohamed, N., Ahmad, M. H., Ismail, Z. and Suhartono. (2010). “Double Seasonal ARIMA Model for Forecasting Load Demand,” MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 217-231. https://doi.org/10.11113/matematika.v26.n.565

Mado, I., Soeprijanto, A., and Suhartono, (2018). “Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia,” International Journal of Electrical and Computer Engineering (IJECE), 4892-4901. http://doi.org/10.11591/ijece.v8i6.pp4892-4901

Ning, Y., Kazemi, H., Tahmasebi, P. (2022). “A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet,” Computers & Geosciences, 164, 105126, https://doi.org/10.1016/j.cageo.2022.105126.

Paningrum, D. (2022). Buku Referensi Investasi Pasar Modal, Kediri: Lembaga Chakra Brahmanda Lentera.

Perone, G. (2020). “An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy,” HEDG - Health Econometrics and Data Group Working Paper Series, University of York. https://doi.org/10.48550/arXiv.2004.00382

Putri, R. N and Setiawan. (2015). “Peramalan Indeks Harga Saham Perusahaan Finansial LQ45 Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) dan Vector Autoregressive (VAR),” Jurnal Sains dan Seni ITS, vol. 4, (2015). http://dx.doi.org/10.12962/j23373520.v4i2.11162

Rahmadianto, B., Lesmana, D.C., Budiarti, R. (2022). Prediksi Harga Saham BBCA dan BMRI dengan Model Seasonal ARIMA”, Undergraduate Thesis, Actuarial, IPB University.

Rifai, N.A.K. (2019). “Pendekatan Regresi Nonparametrik dengan Fungsi Kernel untuk Indeks Harga Saham Gabungan,” Statistika, 19(1), 53-61. https://doi.org/10.29313/jstat.v19i1.4775

Silfiani, M., and Lembang, G.R. (2023). “Perbandingan Peramalan Jumlah Kasus Kecelakaan Lalu Lintas Kota Balikpapan dengan Linear Trend Analysis dan Double Exponential Smoothing,” EQUIVA JOURNAL 1 (1), 14-18. https://doi.org/10.35718/equiva.v1i1.757

Yamacli, D.S., Yamacli, S. (2023). “Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods,” Heliyon, 9(1), https://doi.org/10.1016/j.heliyon.2023.e12796.

Downloads

Published

2023-06-30

How to Cite

Silfiani, M., Hayati, F. N., & Azka, M. (2023). Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting. Jurnal Statistika Dan Komputasi, 2(1), 12–19. https://doi.org/10.32665/statkom.v2i1.1594
Abstract View: 165, PDF Download: 186