Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Predictions Using Prophet | Stock Prices Prediction Project
Stock Prices Prediction Project
course content

Зміст курсу

Stock Prices Prediction Project

bookPredictions 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.

Завдання

  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.

Mark tasks as Completed
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

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.

Завдання

  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.

Mark tasks as Completed
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 1. Розділ 9
AVAILABLE TO ULTIMATE ONLY
some-alt