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Stock Prices Prediction Project
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Stock Prices Prediction Project

Stock Prices Prediction Project

IntroductionImport Necessary Libraries and DataPreliminary ExplorationLet's Plot the Evolution of a Single StockAdd Multiple Stocks to a Single PlotCheck the Consistency of the DataProphetProphet FitPredictions Using ProphetPlot the Results

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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.

Oppgave

Swipe to start coding

  1. Initialize a DF (with 365 days) where to store predictions using the make_future_dataframe() method;
  2. Make the predictions;
  3. Print the last five rows of this dataset.

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Seksjon 1. Kapittel 9
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