Title : Stock Price Forecasting on Time Series Data Using the Long Short-Term Memory (LSTM) Model

Vol 7 No 12 (2024): Volume 07 Issue 12 December 2024

Article Date Published : 10 December 2024 | Page No.: 8866-8875 | Google Scholar | Crossref doi for this article

Abstract :

Stock price forecasting on time series data is a complex task due to the dynamic and uncertain nature of financial markets. This research aims to forecast stock prices by applying an advanced machine learning model, namely Long Short-Term Memory (LSTM), a deep learning architecture that excels in capturing long-term dependencies in time series data. The dataset used in this study consists of 1221 daily ANTM.JK stock price data over the period April 30, 2019 to April 30, 2024. The model was trained and evaluated using performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) in measuring the level of forecasting accuracy. The results show that the LSTM model can accurately predict stock prices on time series data, as evidenced by the MAPE accuracy evaluation value of 2.52% and RMSE of 54.64. These findings indicate that the LSTM model is effective in predicting stock prices on time series data and can be used as a supporting tool in making the right investment decisions.

Keywords :

Accuracy, Forecasting, LSTM, Stock price., Time series.

References :

  1. Abumohsen, M., Owda, A. Y., Owda, M., & Abumihsan, A. (2024). Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation. E-Prime – Advances in Electrical Engineering, Electronics and Energy, 9. https://doi.org/10.1016/j.prime.2024.100636
  2. Lubis, I, S, Br., and Azhar, A, H., (2022). Perancangan Aplikasi Peramalan Penjualan Obat Menggunakan Metode Single Moving Average. Jurnal ITCC (Information Tecnology and Cyber Crime), 1(2).
  3. Anshory, M. I., Priyandari, Y., & Yuniaristanto, Y. (2020). Peramalan Penjualan Sediaan Farmasi Menggunakan Long Short-term Memory: Studi Kasus pada Apotik Suganda. Performa: Media Ilmiah Teknik Industri, 19(2). https://doi.org/10.20961/performa.19.2.45962
  4. Arkadia, A., Hananto, B., & Prasvita, D. S. (2022). Optimasi Long Short Term Memory Dengan Adam Menggunakan Data Udara Kota DKI Jakarta.
  5. Darmawan, R., & Amini, S. (2022). Perbandingan Hasil Sentimen Analysis Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbor pada Twitter Comparison of Sentiment Analysis Results Using Naïve Bayes and K-Nearest Neighbor Algorithm on Twitter. In Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI) Jakarta-Indonesia. https://senafti.budiluhur.ac.id/index.php/
  6. Ensafi, Y., Amin, S. H., Zhang, G., & Shah, B. (2022). Time-series forecasting of seasonal items sales using machine learning – A comparative analysis. International Journal of Information Management Data Insights, 2(1). https://doi.org/10.1016/j.jjimei.2022.100058
  7. He, Q. Q., Wu, C., & Si, Y. W. (2022). LSTM with particle Swam optimization for sales forecasting. Electronic Commerce Research and Applications, 51. https://doi.org/10.1016/j.elerap.2022.101118
  8. Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent Neural Networks for Time Series Forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008
  9. Jange, B., Studi, P., Akuntansi, K., & Riau, D. (2021). Prediksi Harga Saham Bank BCA Menggunakan Prophet. In Journal of Trends Economics and Accounting Research (Vol. 2, Issue 1).
  10. Joseph, R. V., Mohanty, A., Tyagi, S., Mishra, S., Satapathy, S. K., & Mohanty, S. N. (2022). A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting. Computers and Electrical Engineering, 103. https://doi.org/10.1016/j.compeleceng.2022.108358
  11. Kurniawati, A., Sabri Ahmad, M., Fhadli, M., & Lutfi, S. (2023). ANALISIS PERBANDINGAN METODE TIME SERIES FORECASTING UNTUK PREDIKSI PENJUALAN OBAT DI APOTEK (STUDI KASUS: KIMIA FARMA APOTEK TAKOMA) (Vol. 3, Issue 1).
  12. Li, A. W., & Bastos, G. S. (2020). Stock market forecasting using deep learning and technical analysis: A systematic review. In IEEE Access (Vol. 8, pp. 185232–185242). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.3030226
  13. Meng, J., Yang, X., Yang, C., & Liu, Y. (2021). Comparative Analysis of Prophet and LSTM Model in Drug Sales Forecasting. Journal of Physics: Conference Series, 1910(1). https://doi.org/10.1088/1742-6596/1910/1/012059
  14. Pangaribuan, J. J., Fanny, F., Barus, O. P., & Romindo, R. (2023). Prediksi Penjualan Bisnis Rumah Properti Dengan Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA). Jurnal Sistem Informasi Bisnis, 13(2), 154–161. https://doi.org/10.21456/vol13iss2pp154-161
  15. Primawati, A., Sukaesih, I., #2, S., #3, A., & Astuti, D. A. (n.d.). JEPIN (Jurnal Edukasi dan Penelitian Informatika) Perbandingan Kinerja LSTM dan Prophet untuk Prediksi Deret Waktu (Studi Kasus Produksi Susu Sapi Harian).
  16. Rahaman, M. H., Saha, T. K., Masroor, M., Roshani, & Sajjad, H. (2024). Trend analysis and forecasting of meteorological variables in the lower Thoubal river watershed, India using non-parametrical approach and machine learning models. Modeling Earth Systems and Environment, 10(1), 551–577. https://doi.org/10.1007/s40808-023-01799-y
  17. Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr, A., & Kwon, H. H. (2021). Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting. Water Resources Management, 35(12), 4167–4187. https://doi.org/10.1007/s11269-021-02937-w
  18. Saluza, I., Taufikurrahman, M., Widya Astuti, L., & Yulianti, E. (n.d.). Prediksi Harga Saham Menggunakan Empirical Mod … 961.
  19. Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. In SN Computer Science (Vol. 2, Issue 3). Springer. https://doi.org/10.1007/s42979-021-00592-x
  20. Setiawan, Y., & Kartikasari, P. (2022). PREDIKSI HARGA JUAL KAKAO DENGAN METODE LONG SHORT-TERM MEMORY MENGGUNAKAN METODE OPTIMASI ROOT MEAN SQUARE PROPAGATION DAN ADAPTIVE MOMENT ESTIMATION DILENGKAPI GUI RSHINY. 11(1), 99–107. https://ejournal3.undip.ac.id/index.php/gaussian/
  1. Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2022). Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison. IEEE Access, 10, 124715–124727. https://doi.org/10.1109/ACCESS.2022.3224938
  1. Zhang, J., Liu, H., Bai, W., & Li, X. (2024). A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting. North American Journal of Economics and Finance, 69. https://doi.org/10.1016/j.najef.2023.102022

How to Cite :

Stock Price Forecasting on Time Series Data Using the Long Short-Term Memory (LSTM) Model. Daniel Robi Sanjaya, Bayu Surarso, Tarno, 7(12), 8866-8875. Retrieved from https://ijcsrr.org/single-view/?id=20159&pid=20140

Most read articles by the same author(s)

Daniel Robi Sanjaya, Bayu Surarso, Tarno .,

View

86

Citation

Downloads

Article Details