Let’s employ some basic statistical methods to predict stick prices. We will first learn what these methods mean followed by quick code implementations. You’ll be surprised to see that such simple approaches have great accuracies!

This is our second blog under Stock Price Prediction. Our first blog in this series provides an easy-to-understand guide to Facebook Prophet, a Pretrained Model to Forecast Time Series.

Naive Forecast

Naive Forecast is the most basic method of forecasting stock prices. This approach preaches that the forecast is nothing but the value of the variable at a previous timestamp.

For instance, in a dataset where the timestamp is a day, the predicted opening stock price for tomorrow is simply today’s opening value. Though simple, it yields awesome results! Try for yourself!

The reason Naive Forecast works so well is because variables like stock price highly depend on their values in the past. Since sudden changes in the prices of the stock is unlikely. The previous day’s value is usually very close to the following day’s value.

However, the reason this method isn’t widely used is that most of the time, we’d like to predict stock price values for a number of days in the future rather than a single day. This method cannot be used in such cases.

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