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    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

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    282
    Dostępne formaty:
         PDF
         ePub
    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.

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    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

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