Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool
You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production.<br />
By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.<h4>What you will learn</h4>
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Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python<br />
Purchase of the print or Kindle book includes a free PDF eBook
- Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecasts
- Create a forecast and run diagnostics to understand forecast quality
- Fine-tune models to achieve high performance and report this performance with concrete statistics
You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production.<br />
By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.<h4>What you will learn</h4>
- Understand the mathematics behind Prophet’s models
- Build practical forecasting models from real datasets using Python
- Understand the different modes of growth that time series often exhibit
- Discover how to identify and deal with outliers in time series data
- Find out how to control uncertainty intervals to provide percent confidence in your forecasts
- Productionalize your Prophet models to scale your work faster and more efficiently
Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool
You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production.<br />
By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.<h4>What you will learn</h4>
Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python<br />
Purchase of the print or Kindle book includes a free PDF eBook
- Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecasts
- Create a forecast and run diagnostics to understand forecast quality
- Fine-tune models to achieve high performance and report this performance with concrete statistics
You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production.<br />
By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.<h4>What you will learn</h4>
- Understand the mathematics behind Prophet’s models
- Build practical forecasting models from real datasets using Python
- Understand the different modes of growth that time series often exhibit
- Discover how to identify and deal with outliers in time series data
- Find out how to control uncertainty intervals to provide percent confidence in your forecasts
- Productionalize your Prophet models to scale your work faster and more efficiently
35.99
In Stock
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Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool
282
Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool
282
35.99
In Stock
Product Details
| ISBN-13: | 9781837635504 |
|---|---|
| Publisher: | Packt Publishing |
| Publication date: | 03/31/2023 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 282 |
| File size: | 11 MB |
| Note: | This product may take a few minutes to download. |
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