Course Content
Stock Prices Prediction Project
Predictions Using Prophet
Predictions are then made on a DataFrame with a column "ds" containing the dates for which a prediction is to be made. You can get a suitable DataFrame that extends a specified number of days into the future using the helper method Prophet.make_future_dataframe(). By default, it will also include the dates from the history, so we will see the model fit as well.
The predict() method will assign each row in the future a predicted value which it names "yhat". If you pass in historical dates, it will provide an in-sample fit. The forecast object here is a new DataFrame that includes a column "yhat" with the forecast and columns for components and uncertainty intervals.
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- Initialize a DF (with
365days) where to store predictions using themake_future_dataframe()method; - Make the predictions;
- Print the last five rows of this dataset.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Predictions are then made on a DataFrame with a column "ds" containing the dates for which a prediction is to be made. You can get a suitable DataFrame that extends a specified number of days into the future using the helper method Prophet.make_future_dataframe(). By default, it will also include the dates from the history, so we will see the model fit as well.
The predict() method will assign each row in the future a predicted value which it names "yhat". If you pass in historical dates, it will provide an in-sample fit. The forecast object here is a new DataFrame that includes a column "yhat" with the forecast and columns for components and uncertainty intervals.
Swipe to show code editor
- Initialize a DF (with
365days) where to store predictions using themake_future_dataframe()method; - Make the predictions;
- Print the last five rows of this dataset.
Switch to desktop for real-world practiceContinue from where you are using one of the options below