Stock Forecasting Using Facebook Prophet And Time Series

The purpose of this article is to demonstrate how to forecast time series using Prophet, a powerful library developed by Facebook.

Historical stock prices can provide valuable insight into future stock prices, but they cannot always predict whether a stock price will rise or fall. As a result, we will utilize machine learning and our own variables to make more informed predictions.

Facebook’s research team developed Prophet, an innovative forecasting tool. As opposed to other forecasting methods, it is easy to use and designed for both data scientists and non-data scientists. Because Prophet uses Scikit-Learn’s API, Python users can easily use it. The Prophet Blog provides more information about Prophet.

Time series forecasting can be made easier with Prophet’s unique features. The method is best suited for hourly, daily, or weekly observations with at least a few months of historical data. In addition to predicting strong seasonalities, it can also predict holidays that occur at irregular intervals, such as days of the week or times of the year. A reasonable number of missing observations or large outliers can also benefit from it. Furthermore, it can identify non-linear growth curves that reach a natural limit or saturation point.

Download the source code using the link below!