Stock Price Prediction using Prophet Facebook Algorithm for BBCA and TLKM

Sasmitoh Rahmad Riady

doi:10.25008/ijadis.v4i2.1258

Authors

Sasmitoh Rahmad Riady Faculty of Informatics, Bina Insani University, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v4i2.1258

Keywords:

Prophet Facebook, Stock Price, Prediction, RMSE, MSE and MAE

Abstract

Stocks are an investment instrument that is starting to be in great demand by the public today. However, stock prices are fluctuating, making people feel doubts about when they are going to invest. To overcome these doubts, we need a way to predict stock prices. This study aims to predict stock price fluctuations using Facebook's Prophet Algorithm to help people decide their investment in stock. The research object used is BBCA and TLKM stock price data in the form of a time series from 03 May 2021 to 28 April 2022 with stock price testing data for the next week, namely 01 May 2022 to 07 May 2022. From the training and testing process done, a prediction is produced that is very close to the original value. Using the RMSE, MSE and MAE measurements, we get RMSE 49.6, MSE 2462.1 and MAE 37.5 for BBCA and RMSE stocks, namely 21.3, MSE 456.5 and MAE 19.2 for TLKM shares. The conclusion is that Facebook's Prophet Algorithm is suitable for predicting stock prices.

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References

W. Yang, B. Wang, and R. Wang, "Detection of Anomaly Stock Price Based on Time Series Deep Learning Models," pp. 110-114, 2020. https://doi.org/10.1109/MSIEID52046.2020.00029

Y. Guo, S. Han, C. Shen, Y. Li, X. Yin, and Y. Bai, "An Adaptive SVR for High-Frequency Stock Price Forecasting," vol. XX, no. c, pp. 1-8, 2018. https://doi.org/10.1109/ACCESS.2018.2806180

W. Fang, W. Lin, Y. Wang, and A. F. Prophet, "Combine Facebook Prophet and LSTM with BPNN Forecasting financial markets?: the Morgan Taiwan Index," pp. 0-1, 2019. https://doi.org/10.1109/ISPACS48206.2019.8986377

A. Sethia and P. Raut, Application of LSTM , GRU and ICA for Stock Price Prediction. Springer Singapore.

M. Sinaga, "Optimization of SV-kNNC using Silhouette Coefficient and LMKNN for Stock Price Prediction," pp. 326-331.

I. Zuhroh, M. Rofik, and A. Echchabi, "Banking stock price movement and macroeconomic indicators?: k-means clustering approach Banking stock price movement and macroeconomic indicators?: k-means clustering approach," Cogent Bus. Manag., vol. 8, no. 1, 2021. https://doi.org/10.1080/23311975.2021.1980247

P. T. Yamak, "A Comparison between ARIMA , LSTM , and GRU for Time Series Forecasting," 2017.

E. Zunic and D. Donko, "A LGORITHM FOR S UCCESSFUL S ALES F ORECASTING B ASED ON R EAL - WORLD D ATA," no. May, 2020.

S. Patandung and I. Jatnika, "The FB Prophet Model Application to the Growth Prediction of International Tourists in Indonesia during the COVID-19 Pandemic," vol. 6, no. 2, pp. 110-115, 2021.

S. R. Riady, T. W. Sen, and I. Technology, "Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique," vol. 04, no. 01, pp. 90-95, 2021. https://doi.org/10.31326/jisa.v4i1.904

M. S. Salman, O. Kukrer, and A. Hocanin, "Recursive inverse algorithm: Mean-square-error analysis," Digit. Signal Process. A Rev. J., vol. 66, pp. 10-17, 2017. https://doi.org/10.1016/j.dsp.2017.04.001

M. Vijh, D. Chandola, V. A. Tikkiwal, and A. Kumar, "ScienceDirect Stock Closing Closing Price Price Prediction Prediction using using Machine Machine Learning Learning Techniques Techniques," Procedia Comput. Sci., vol. 167, no. 2019, pp. 599-606, 2020. https://doi.org/10.1016/j.procs.2020.03.326

A. Garlapati, "Stock Price Prediction Using Facebook Prophet and Arima Models," pp. 1-7, 2021. https://doi.org/10.1109/I2CT51068.2021.9418057

B. K. Jha and S. Pande, "Time Series Forecasting Model for Supermarket Sales using FB-Prophet," no. Iccmc, pp. 547-554, 2021.

A. Subasi, F. Amir, K. Bagedo, A. Shams, and A. Sarirete, "Stock market prediction using machine learning," Procedia Computer Science, vol. 194, pp. 173-179, 2021. https://doi.org/10.1016/j.procs.2021.10.071

S. Forecasting, "Machine-Learning Models for Sales Time," pp. 1-11, 2019.

J. Chou, "Multistep energy consumption forecasting by metaheuristic optimization of time-series analysis and machine learning," no. June, pp. 1-32, 2020.

R. Chuentawat and Y. Kan-ngan, "The Comparison of PM2 . 5 forecasting methods in the form of multivariate and univariate time series based on Support Vector Machine and Genetic Algorithm," 2018 15th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., pp. 572-575, 2018.

https://doi.org/10.1109/ECTICon.2018.8619867

C. Beneditto, A. Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, "ScienceDirect," Procedia Comput. Sci., vol. 179, no. 2020, pp. 524-532, 2021.

https://doi.org/10.1016/j.procs.2021.01.036

V. Olsavszky, M. Dosius, C. Vladescu, and J. Benecke, "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," pp. 16-18, 2020.

https://doi.org/10.3390/ijerph17144979