Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - S

Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - S - Opis

Forecasting Time Series Data with Prophet will help you to implement Prophet's cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community.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.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. Spis treści: 0. Product Information Document1. The History and Development of Time Series Forecasting2. Getting Started with Prophet3. How Prophet Works4. Handling Non-Daily Data5. Working with Seasonality6. Forecasting Holiday Effects7. (...) więcej Controlling Growth Modes8. Influencing Trend Changepoints9. Including Additional Regressors10. Accounting for Outliers and Special Events11. Managing Uncertainty Intervals12. Performing Cross-Validation13. Evaluating Performance Metrics14. Productionalizing Prophet O autorze: Greg Rafferty is a data scientist at Google in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Facebook (Meta) and IBM. Greg has been an instructor in business analytics on Coursera and has led face-to-face workshops with industry professionals in data science and analytics. With both an MBA and a degree in engineering, he is able to work across the spectrum of data science and communicate with both technical experts and non-technical consumers of data alike. mniej