COMPARISON OF WEIGHTED MOVING AVERAGE AND PROPHET METHOD IN PREDICTING STOCK PRICES

Gilbert Alfoin, Rosita Herawati Rosita Herawati

Abstract


Stocks are one of the favorite investment methods of Indonesian people. This is because stocks are "high risk high return" investments. That is an investment that provides high returns even though it has a high risk as well. To find a good stock, we can do technical analysis. But doing technical analysis is not easy because it takes time and enough experience to be able to do the right technical analysis. To overcome difficulties in conducting technical analysis. An appropriate algorithm is needed to predict stock prices. And a program that can work automatically in running the algorithm. So that's why I created a program that can run Weighted Moving Average and Prophet automatically. Later these two algorithms will be compared for their accuracy in predicting stock prices. The final result of this study is the performance of the Weighted Moving Average and Prophet in predicting stock prices. With this research, readers can understand how the Weighted Moving Average and Prophet work. And it is easier to predict stock prices because it can be done automatically.


Keywords


stock; wma; prophet

Full Text:

PDF

References


M. S. A. A. Hilhami, “Forecasting Harga Saham Pt. Astra Agro Lestari Dengan Metode Simple Moving

Average Dan Weighted Moving Average,” 2021.

D. P. Sugumonrong and D. A. Gultom, “Perbandingan Metode Moving Average (MA) Dan Neural

Network yang Berbasis Algoritma Backpropagation Dalam Prediksi Harga Saham,” J. Inf. …, vol. 3, no. 2, pp.

–150, 2018, [Online]. Available: https://ejournal.medan.uph.edu/index.php/isd/article/view/357.

A. Suarsa, “Perbandingan Analisa Teknikal Metode Simple Moving Average, Weigted Moving Average,

Dan Exponential Moving Average Dalam Memprediksi Harga Saham Lq-45 Sub Sektor Telekomunikasi,”

Dini Indriyani Putri, Agung Budi Prasetijo, and Adian Fatchur Rochim, “Prediksi Harga Saham

Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-

Marquardt,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 1, pp. 11–18, 2021, doi:

22146/jnteti.v10i1.678. Di Bursa Efek Jakarta,” no. May, 2017, doi: 10.5281/zenodo.581805.

Y. D. Saputra, D. A. I. Maruddani, and A. Hoyyi, “Analisis Teknikal Saham Dengan Indikator Gabungan

Weighted Moving Average Dan Stochastic Oscillator,” J. Gaussian, vol. 8, no. 1, pp. 1–11, 2019, doi:

14710/j.gauss.v8i1.26617.

S. Kulshreshtha and A. Vijayalakshmi, “An ARIMA-LSTM hybrid model for stock market prediction

using live data,” J. Eng. Sci. Technol. Rev., vol. 13, no. 4, pp. 117– 123, 2020, doi: 10.25103/jestr.134.11.

W. N. Chan, “Time Series Data Mining: Comparative Study of ARIMA and Prophet Methods for

Forecasting Closing Prices of Myanmar Stock Exchange,” J. Comput. Appl. Res., vol. 1, no. 1, pp. 75–80, 2020.

C. Chandra and S. Budi, “Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset

Pendaftaran Mahasiswa Baru,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 278–287, 2020, doi:

28932/jutisi.v6i2.2676.

V. Milosavljevic, “Stock market price prediction using time series models National College of Ireland

Supervisor : Vladimir Milosavljevic.”

M. Mazed, “Stock Price Prediction Using Time Series Data,” Brac Univ., vol. 1(1), no. August, pp. 1–51,




DOI: https://doi.org/10.24167/proxies.v3i2.12429

Copyright (c) 2024 Proxies : Jurnal Informatika



View My Stats