Tarek A. Atwan

Time Series Analysis with Python Cookbook (eBook, ePUB)

Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection

• Format: ePub

Produktbeschreibung

To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You'll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.
You'll hit the ground running 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.
Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you'll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 812
• Erscheinungstermin: 23. Januar 2026
• Englisch
• ISBN-13: 9781805122999
• Artikelnr.: 76236666

Autorenporträt

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 various universities in the United States. He is regarded as a data science mentor and advisor, working with executive leaders in numerous industries to solve complex problems using a data-driven approach.