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    Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition Tarek A. Atwan

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition Tarek A. Atwan - okladka książki

    Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition Tarek A. Atwan - okladka książki

    Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition Tarek A. Atwan - audiobook MP3

    Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition Tarek A. Atwan - audiobook CD

    Autor:
    Tarek A. Atwan
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Stron:
    98
    To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples.

    You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.

    Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

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    O autorze książki

    Tarek A. Atwan is a data analytics expert with over 16 years of international consulting experience, providing subject matter expertise in data science, machine learning operations, data engineering, and business intelligence. He has taught multiple hands-on coding boot camps, courses, and workshops on various topics, including data science, data visualization, Python programming, time series forecasting, and blockchain at different universities in the United States. He is regarded as an industry mentor and advisor, working with executive leaders in various industries to solve complex problems using a data-driven approach.

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