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Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition - Ediciones

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Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition

Editorial: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)

ISBN: 978-18-3763-939-7

ISBN sin guiones: 9781837639397

Tipo de cubierta: Softcover

Páginas: 1

Idioma: polaco (Polonia)

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Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition

Ben Auffarth

The Python time-series ecosystem is a huge and challenging topic to tackle, especially for time series since there are so many new libraries and models. Machine Learning for Time Series, Second Edition, aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and helping you build better predictive systems. This fully updated second edition starts by re-introducing the basics of time series and then helps you get to grips with traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will gain a deeper understanding of loading time-series datasets from any source and a variety of models, such as deep learning recurrent neural networks, causal convolutional network models, and gradient boosting with feature engineering. This book will also help you choose the right model for the right problem by explaining the theory behind several useful models. New updates include a chapter on forecasting and extracting signals on financial markets and case studies with relevant examples from operations management, digital marketing, and healthcare. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time series.