Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI

This research aims to develop a stock price forecasting application using time series analysis with the Prophet model. The application retrieves historical stock data from Yahoo Finance (2015–present) for Indonesian stocks, which is then processed and analyzed to predict future prices. The study integrates yfinance for data collection, Prophet for forecasting, and Plotly for visualizing the results. The application allows users to select stocks and customize prediction periods (1–4 years). The findings indicate that while the model provides useful short-term predictions, its accuracy is limited by market volatility and external factors. This tool can support decision-making but should be used in conjunction with other forecasting methods.

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Publication Details

Journal
Formosa Journal of Science and Technology
Published
2024-11-21
DOI
https://doi.org/10.55927/fjst.v3i11.12216
Citations
1
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI

Kartika Dewi Sri Susilowati, Edra Arkananta Gultom, Anik Kusmintarti
1 citations
Formosa Journal of Science and Technology
Computational Physics and Python Applications
0.35
article

Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI

Kartika Dewi Sri Susilowati, Edra Arkananta Gultom, Anik Kusmintarti
article en
1 citations

Abstract

This research aims to develop a stock price forecasting application using time series analysis with the Prophet model. The application retrieves historical stock data from Yahoo Finance (2015–present) for Indonesian stocks, which is then processed and analyzed to predict future prices. The study integrates yfinance for data collection, Prophet for forecasting, and Plotly for visualizing the results. The application allows users to select stocks and customize prediction periods (1–4 years). The findings indicate that while the model provides useful short-term predictions, its accuracy is limited by market volatility and external factors. This tool can support decision-making but should be used in conjunction with other forecasting methods.

Formosa Journal of Science and TechnologyVol. 3(11)
State University of Malang (ID)
Openalex Percentile: Top 31%
Computational Physics and Python Applications
0.35
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