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    ADVANCED FORECASTING WITH PYTHON

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    Sinopse

    Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models.

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    ISBN9781484271490
    SubtítuloWITH STATE-OF-THE-ART-MODELS INCLUDING LSTMS, FACEBOOK'S PROPHET, AND AMAZON'S DEEPAR
    Pré vendaNão
    Peso550g
    Autor para link
    Livro disponível - pronta entregaNão
    Dimensões25.4 x 17.78 x 1.68
    IdiomaInglês
    Tipo itemLivro Importado
    Número de páginas296
    Número da edição1ª EDIÇÃO - 2021
    Código Interno958985
    Código de barras9781484271490
    AcabamentoPAPERBACK
    AutorKORSTANJE, JOOS
    EditoraAPRESS
    Sob encomendaSim

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