Forecasting stock prices has always been an attractive but challenging topic in financial analysis. One popular approach in time series analysis is the autoregressive (AR) model. In this tutorial, we'll explore how to use an AR model to predict the stock prices of two tech giants, Apple and Microsoft.
What is autoregressive?
Autoregressive is a time series model that uses observations at previous points in time to predict the next point in time. In short, it assumes that the value of t at a particular time depends linearly on previous values of p.
advantage:
Simplicity: AR models are easy to understand and implement.
Efficiency: They can be very accurate for certain datasets that exhibit certain patterns over time.
shortcoming:
Assumptions: AR assumes linearity and may fail to capture complex relationships.
Stationarity: A dataset should be stationary, meaning that statistical properties such as mean and variance remain constant over time.
Why Use AR for Stock Analysis? Stock prices are continuous and often depend on past prices, making AR a potential candidate. However, remember that stock prices can be affected by countless external factors, so always combine AR with other technologies and insights.
Need to import library
Before proceeding with the rest, you need to install or create a virtual environment (pure Python or Conda, Mumba). You can also try opening the code in Google Colab (copy the link from the gist at the end of the article).
Libraries that need to be installed:
pip install numpy
pip install pandas
pip install matplotlib
pip install statsmodels
pip install yfinance
Libraries that need to be imported:
# Import Necessary Librar