This video tutorial is a complete walkthrough on how to do quick stock price forecasting with ARIMA models in R. We will forecast the future values of SPY (the S&P 500 ETF) with daily close price data from Yahoo Finance. I will show you how to get the daily data in XTS (time series) format, explain XTS formatting and then walk you through a quick and easy forecasting process that uses 4 models (auto.arima, 2 custom arimas, and a typical default arima) for comparison.

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As you will see in the video the stock price forecasts are very high accuracy (determined by MAPE and numerous other indicators). An important note that I discuss and make clear in the video is to use this forecasted stock price data in a directional manner. Don't try and forecast the exact price each day or similar. Also, don't try forecasting exact gains on this time series. There are far too many external and internal influencers that are highly volatile and can really give you wrong forecasting data. Instead look at a range and the general direction. Is it positive, negative or flat? With the SPY stock price forecast we see that the models all point to a positive or flat trend for the next 100 days (period I selected in the term variable).

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This same arima forecasting process can be used for stock price forecasting with any publicly listed stock on Yahoo Finance. You can use IBM, AAPL, XOM, etc... Try some different ETFs (better diversification) and mutual funds or stocks and have fun seeing where they are trending over the next few months. Also feel free to change from past 5 years of time series data to 10 or 20 years to include some recessions and see how or if that changes the forecasted trends.

I hope you found this both informational and entertaining.

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