Stock Market Price Prediction

Chandramouli Yalamanchili
Updated - 06/05/2021 [Created - 03/28/2021]
View Project Code on GitHub

Introduction

The goal of this project is to use ARIMA, Auto Regressive Integrated Moving Average) model to do time series prediction for stock price using the historic data as input.
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Input Dataset

• https://finance.yahoo.com/quote/AAPL/history?period1=1465084800&period2=1622851200&interval=1d&filter=history&frequency=1d&includeAdjustedClose=true
• I have pulled the Apple stock price by day for last 5 years in the form of CSV and used it for the project.
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Prerequisite

• Python 3 (or Anaconda distribution with Python 3)
• Jupyter notebook
• Python Packages needed
  • os
  • Pandas
  • Numpy
  • Seaborn
  • Matplotlib
  • subprocess
  • scikit-learn
  • ARIMA model from statsmodels.tsa.arima_model
  • datetime

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Usage

• Download the dataset from Yahoo finance website using the link provided and place it in the path ../Data/APPL.csv
• Install all of the Python packages needed.
• Execute the jupyter notebook in the Jupyter server of choice.
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Method Used

• Used Python and Jupyter notebook for this project.
• Perform data visualization and summary functions to understand the trends for the Apple stoc price.
• Used ARIMA model to predict the future values for the stock price.
• Trained the ARIMA model with training data and ran the tests to measure how the model is performing.
• Finally plotted the test values vs. predicted values.
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Conclusion

• ARIMA model seem to perform really well in predicting the future stock price values.
• Both the Mean Squared Error and Symmetric mean absolute percentage error values are low indicating that the model has performed good.
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References

• https://www.kaggle.com/pierpaolo28/stock-market-analysis-and-time-series-prediction/
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Authors

• Chandramouli Yalamanchili
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