Course Content
Stock Prices Prediction Project
Prophet
The prophet is a Python library used for forecasting time series data. It is open-source and developed by Facebook. It is based on a decomposable time series model with three main components: trend, seasonality, and holidays.
Prophet uses a decomposable model, where the time series is broken down into trends, seasonality, and holidays. The trend component models non-periodic changes, the seasonal component models periodic changes, and the holiday component models the effects of events such as Christmas, Black Friday, etc. The library can take into account additional regressors as well.
Prophet also includes functionality for handling missing data and outliers and automatically fitting the model using the Bayesian inference. It also provides tools for analyzing the model's forecast errors, which can help identify any patterns or sources of error in the data.
Overall, prophet is designed to make it easy to create high-quality forecasts with minimum tuning and effort. It is particularly useful for business forecasting and other applications that involve time series data.
Task
- Select only the rows with the
AAPLstock; - Extract only the
"date"and"close"columns; - Rename
"date"into"ds"and"close"into"y"(standard names required by aprophet).
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The prophet is a Python library used for forecasting time series data. It is open-source and developed by Facebook. It is based on a decomposable time series model with three main components: trend, seasonality, and holidays.
Prophet uses a decomposable model, where the time series is broken down into trends, seasonality, and holidays. The trend component models non-periodic changes, the seasonal component models periodic changes, and the holiday component models the effects of events such as Christmas, Black Friday, etc. The library can take into account additional regressors as well.
Prophet also includes functionality for handling missing data and outliers and automatically fitting the model using the Bayesian inference. It also provides tools for analyzing the model's forecast errors, which can help identify any patterns or sources of error in the data.
Overall, prophet is designed to make it easy to create high-quality forecasts with minimum tuning and effort. It is particularly useful for business forecasting and other applications that involve time series data.
Task
- Select only the rows with the
AAPLstock; - Extract only the
"date"and"close"columns; - Rename
"date"into"ds"and"close"into"y"(standard names required by aprophet).
Switch to desktop for real-world practiceContinue from where you are using one of the options below