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Time Series Analysis with Python Cookbook
Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection , Second Edition

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Profile Icon Tarek A. Atwan
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Early Access Early Access Publishing in Jan 2026
€18.99 per month
eBook Jan 2026 812 pages 2nd Edition
eBook
€8.98 €35.99
Paperback
€44.99
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Arrow left icon
Profile Icon Tarek A. Atwan
Arrow right icon
Early Access Early Access Publishing in Jan 2026
€18.99 per month
eBook Jan 2026 812 pages 2nd Edition
eBook
€8.98 €35.99
Paperback
€44.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.98 €35.99
Paperback
€44.99
Subscription
Free Trial
Renews at €18.99p/m

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Time Series Analysis with Python Cookbook

Reading Time Series Data from Databases

Time series data can be stored in a variety of formats, including flat files (such as CSV, Excel, or Parquet) or database systems. In Chapter 1, we explored reading data from different file types, whether stored locally (on-premise) or remotely in a cloud service such as AWS S3. In this chapter, we will focus on how to retrieve time series data from different database systems.

Databases extend storage capabilities beyond flat files, supporting structured, semi-structured, and unstructured data—including text, images, and media files. They are optimized for efficient read-and-write operations at scale, making them ideal for handling terabytes or even petabytes of data with efficient and optimized data retrieval capabilities.

Modern data architectures typically distinguish between several database paradigms:

  • Transactional (OLTP) databases are optimized for handling day-to-day business operations with many small, concurrent...

Reading data from a relational database

Relational databases such as PostgreSQL are widely used for structured time series data due to their ACID compliance, SQL support, and robust indexing capabilities. In this recipe, you’ll extract time series data from PostgreSQL using two Python approaches:

  1. Low-level connection: Use psycopg2 (PostgreSQL’s Python adapter) to execute SQL queries and load results into a pandas DataFrame.
  2. High-level ORM: Leverage SQLAlchemy, an object-relational mapper (ORM) integrated with pandas, for more abstracted and maintainable queries.

Getting ready

In this recipe, we’ll use PostgreSQL version 18.0, the latest stable version as of this writing. The code examples should be compatible with recent PostgreSQL releases.

To connect to and query the database in Python, you will need to install psycopg, a popular PostgreSQL database adapter for Python. You will also need to install SQLAlchemy, which provides...

Reading data from Snowflake

A very common place to extract data for analytics is a company’s data warehouse. Data warehouses host a massive amount of data that, in most cases, contains integrated data to support various reporting and analytics needs, in addition to historical data from various source systems.

The evolution of the cloud brought us modern cloud data warehouses such as Amazon Redshift, Google BigQuery, Azure SQL Data Warehouse, and Snowflake. Unlike traditional on-premise data warehouses, modern cloud data warehouses offer elastic scalability, separating storage and compute, with built-in features for handling semi-structured data.

In this recipe, you will work with Snowflake, a powerful Software as a Service (SaaS) cloud-based data warehousing platform that can be hosted on different cloud platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. You will learn how to connect to Snowflake using Python to extract time...

Reading data from MongoDB

MongoDB, a NoSQL database, stores data in documents and uses Binary JSON (BSON), which extends JSON with additional data types and is more efficient for storage and retrieval. Unlike relational databases, where data is stored in tables that consist of rows and columns, document-oriented databases store data in collections and documents.

A document represents the lowest granular level of data being stored, as rows do in relational databases. A collection, like a table in relational databases, stores documents. Unlike relational databases, a collection can store documents of different schemas and structures, making MongoDB’s flexible schema ideal for time series data that might evolve with changing metrics or collection patterns.

Since version 5.0, MongoDB offers dedicated time series collections that efficiently store and organize time series data. These specialized collections automatically organize measurements by time and metadata (such as...

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Key benefits

  • Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
  • Learn different techniques for evaluating, diagnosing, and optimizing your models
  • Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

Description

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.

Who is this book for?

This 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. To 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.

What you will learn

  • Understand what makes time series data different from other data
  • Apply imputation and interpolation strategies to handle missing data
  • Implement an array of models for univariate and multivariate time series
  • Plot interactive time series visualizations using hvPlot
  • Explore state-space models and the unobserved components model (UCM)
  • Detect anomalies using statistical and machine learning methods
  • Forecast complex time series with multiple seasonal patterns
  • Use conformal prediction for constructing prediction intervals for time series

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 23, 2026
Length: 812 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805122999
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Product Details

Publication date : Jan 23, 2026
Length: 812 pages
Edition : 2nd
Language : English
ISBN-13 : 9781805122999
Category :
Languages :
Concepts :

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Table of Contents

17 Chapters
Reading Time Series Data from Files Chevron down icon Chevron up icon
Reading Time Series Data from Databases Chevron down icon Chevron up icon
Persisting Time Series Data to Files Chevron down icon Chevron up icon
Persisting Time Series Data to Databases Chevron down icon Chevron up icon
Working with Date and Time in Python Chevron down icon Chevron up icon
Handling Missing Data Chevron down icon Chevron up icon
Outlier Detection Using Statistical Methods Chevron down icon Chevron up icon
Exploratory Data Analysis and Diagnosis Chevron down icon Chevron up icon
Building Univariate Time Series Models Using Statistical Methods Chevron down icon Chevron up icon
Additional Statistical Modeling Techniques for Time Series Chevron down icon Chevron up icon
Forecasting Using Supervised Machine Learning Chevron down icon Chevron up icon
Deep Learning for Time Series Forecasting Chevron down icon Chevron up icon
Outlier Detection Using Unsupervised Machine Learning Chevron down icon Chevron up icon
Advanced Techniques for Complex Time Series Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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