×
Dodano do koszyka:
Pozycja znajduje się w koszyku, zwiększono ilość tej pozycji:
Zakupiłeś już tę pozycję:
Książkę możesz pobrać z biblioteki w panelu użytkownika
Pozycja znajduje się w koszyku
Przejdź do koszyka

Zawartość koszyka

ODBIERZ TWÓJ BONUS :: »

Forecasting Time Series Data with Facebook Prophet. Build, improve, and optimize time series forecasting models using the advanced forecasting tool

(ebook) (audiobook) (audiobook) Książka w języku 1
Autor:
Greg Rafferty
Forecasting Time Series Data with Facebook Prophet. Build, improve, and optimize time series forecasting models using the advanced forecasting tool Greg Rafferty - okladka książki

Forecasting Time Series Data with Facebook Prophet. Build, improve, and optimize time series forecasting models using the advanced forecasting tool Greg Rafferty - okladka książki

Forecasting Time Series Data with Facebook Prophet. Build, improve, and optimize time series forecasting models using the advanced forecasting tool Greg Rafferty - audiobook MP3

Forecasting Time Series Data with Facebook Prophet. Build, improve, and optimize time series forecasting models using the advanced forecasting tool Greg Rafferty - audiobook CD

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
270
Dostępne formaty:
     PDF
     ePub
     Mobi

Ebook (29,90 zł najniższa cena z 30 dni)

149,00 zł (-10%)
134,10 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

(29,90 zł najniższa cena z 30 dni)

Przenieś na półkę

Do przechowalni

Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet’s cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you 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 and see some useful features when running Prophet in production environments.
By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.

Wybrane bestsellery

O autorze książki

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.

Packt Publishing - inne książki

Zamknij

Przenieś na półkę

Proszę czekać...
ajax-loader

Zamknij

Wybierz metodę płatności

Ebook
134,10 zł
Dodaj do koszyka
Zamknij Pobierz aplikację mobilną Ebookpoint