{"product_id":"time-series-analysis-with-python-cookbook-second-edition-practical-recipes-for-the-complete-time-series-workflow-from-modern-data-engineering-to-a-paperback","title":"Time Series Analysis with Python Cookbook - Second Edition: Practical recipes for the complete time series workflow, from modern data engineering to a - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eTarek A. Atwan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePerform time series analysis and forecasting confidently with this Python code bank and reference manual.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAccess exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms\u003c\/p\u003e\u003cp\u003e- Learn different techniques for evaluating, diagnosing, and optimizing your models\u003c\/p\u003e\u003cp\u003e- Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eTo 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003eThrough 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Understand what makes time series data different from other data\u003c\/p\u003e\u003cp\u003e- Apply imputation and interpolation strategies to handle missing data\u003c\/p\u003e\u003cp\u003e- Implement an array of models for univariate and multivariate time series\u003c\/p\u003e\u003cp\u003e- Plot interactive time series visualizations using hvPlot\u003c\/p\u003e\u003cp\u003e- Explore state-space models and the unobserved components model (UCM)\u003c\/p\u003e\u003cp\u003e- Detect anomalies using statistical and machine learning methods\u003c\/p\u003e\u003cp\u003e- Forecast complex time series with multiple seasonal patterns\u003c\/p\u003e\u003cp\u003e- Use conformal prediction for constructing prediction intervals for time series\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes.\u003c\/p\u003e\u003cp\u003eTo get the most out of this book, you'll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help you to better utilize and apply the recipes more quickly.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Getting Started with Time Series Analysis\u003c\/p\u003e\u003cp\u003e- Reading Time Series Data from Files\u003c\/p\u003e\u003cp\u003e- Reading Time Series Data from Databases\u003c\/p\u003e\u003cp\u003e- Persisting Time Series Data to Files\u003c\/p\u003e\u003cp\u003e- Persisting Time Series Data to Databases\u003c\/p\u003e\u003cp\u003e- Working with Date and Time in Python\u003c\/p\u003e\u003cp\u003e- Handling Missing Data\u003c\/p\u003e\u003cp\u003e- Outlier Detection Using Statistical Methods\u003c\/p\u003e\u003cp\u003e- Exploratory Data Analysis and Diagnosis\u003c\/p\u003e\u003cp\u003e- Building Univariate Models Using Statistical Methods\u003c\/p\u003e\u003cp\u003e(N.B. Please use the Read Sample option to see further chapters)\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 812\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.61 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 23, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":53042519703859,"sku":"9781805124283","price":93.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/GJARJzKY5K9781805124283.webp?v=1770837006","url":"https:\/\/www.vysn.com\/en-ca\/products\/time-series-analysis-with-python-cookbook-second-edition-practical-recipes-for-the-complete-time-series-workflow-from-modern-data-engineering-to-a-paperback","provider":"VYSN","version":"1.0","type":"link"}