×
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 Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - Second Edition

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

Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - Second Edition Greg Rafferty - okladka książki

Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - Second Edition Greg Rafferty - audiobook MP3

Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - Second Edition Greg Rafferty - audiobook CD

Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
282
Dostępne formaty:
     PDF
     ePub
Zostało Ci na świąteczne zamówienie opcje wysyłki »
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.

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
116,10 zł
Dodaj do koszyka
Zamknij Pobierz aplikację mobilną Ebookpoint