book

Time Series Analysis with Python Cookbook

Name: Time Series Analysis with Python Cookbook
Author: Tarek A. Atwan
ISBN: 9781801075541

by Tarek A. Atwan

June 2022

Beginner to intermediate

630 pages

13h 18m

English

Packt Publishing

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Time Series Analysis with Python Cookbook
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this book Download the example code filesCode in ActionConventions usedSectionsGetting readyHow to do it…How it works…There's more…See alsoGet in touchShare Your Thoughts
Chapter 1: Getting Started with Time Series Analysis
Technical requirementsDevelopment environment setupGetting readyHow to do it…How it works…There's more…See alsoInstalling Python librariesGetting readyHow to do it…How it works…There's more…See alsoInstalling JupyterLab and JupyterLab extensionsGetting readyHow to do it…How it works…There's more…See also
Chapter 2: Reading Time Series Data from Files
Technical requirementsReading data from CSVs and other delimited filesGetting readyHow to do it…How it works…There's more…See alsoReading data from an Excel fileGetting readyHow to do it…How it works…There's more…See alsoReading data from URLsGetting readyHow to do it…How it works…There's more…See alsoReading data from a SAS datasetGetting readyHow to do it…How it works…There's more…See also
Chapter 3: Reading Time Series Data from Databases
Technical requirementsReading data from a relational databaseGetting readyHow to do it…How it works…There's more…See alsoReading data from SnowflakeGetting readyHow to do it...How it works...There's more...See alsoReading data from a document database (MongoDB)Getting readyHow to do it…How it works…There's more…See alsoReading third-party financial data using APIsGetting readyHow to do it…How it works…There's more…See alsoReading data from a time series database (InfluxDB)Getting readyHow to do it…How it works…There's more…See also
Chapter 4: Persisting Time Series Data to Files
Technical requirementsSerializing time series data with pickleGetting readyHow to do it…How it works…There's more…See alsoWriting to CSV and other delimited filesGetting readyHow to do it…How it works…There's more…See alsoWriting data to an Excel fileGetting readyHow to do it…How it works…There's more…See alsoStoring data to S3Getting readyHow to do it…How it works…There's more…See also
Chapter 5: Persisting Time Series Data to Databases
Technical requirementsWriting time series data to a relational database (PostgreSQL and MySQL)Getting readyHow to do it…How it works…There's more…See alsoWriting time series data to MongoDBGetting readyHow to do it…How it works…There's more…See alsoWriting time series data to InfluxDBGetting readyHow to do it…How it works…There's more…See alsoWriting time series data to SnowflakeGetting readyHow to do it...How it works...There's more...See also
Chapter 6: Working with Date and Time in Python
Technical requirementsWorking with DatetimeIndexHow to do it…How it works…There's more…See alsoProviding a format argument to DateTimeHow to do it…How it works…There's more…See alsoWorking with Unix epoch timestampsHow to do it…How it works…There's more…See alsoWorking with time deltasHow to do it…How it works…There's more…See alsoConverting DateTime with time zone informationHow to do it…How it works…There's more… See alsoWorking with date offsetsHow to do it…How it works…There's more…See also Working with custom business daysHow to do it…How it works…There's more…See also
Chapter 7: Handling Missing Data
Technical requirementsFunction 1 – read_datasetsFunction 2 – plot_dfsFunction 3 – rmse_scoreUnderstanding missing dataPerforming data quality checksGetting readyHow to do it…How it works…There's more…See alsoHandling missing data with univariate imputation using pandas Getting readyHow to do it…How it works…There's more…See alsoHandling missing data with univariate imputation using scikit-learn Getting readyHow to do it…How it works…There's more…See alsoHandling missing data with multivariate imputationGetting readyHow to do it…How it works…There's more…See alsoHandling missing data with interpolationGetting readyHow to do it…How it works…There's more…See also
Chapter 8: Outlier Detection Using Statistical Methods
Technical requirementsUnderstanding outliersResampling time series dataHow to do it…How it works…There's more...See alsoDetecting outliers using visualizationsGetting readyHow to do it...How it works...There's more...See alsoDetecting outliers using the Tukey methodHow to do it...How it works...There's more...See alsoDetecting outliers using a z-scoreHow to do it...How it works...There's more...See alsoDetecting outliers using a modified z-scoreHow to do it...How it works...There's more...See also

Chapter 9: Exploratory Data Analysis and Diagnosis
Technical requirementsPlotting time series data using pandasGetting readyHow to do it…How it works…There's more…See alsoPlotting time series data with interactive visualizations using hvPlot Getting readyHow to do it…How it works…There's more…See alsoDecomposing time series dataGetting readyHow to do it…How it works…There's more…See alsoDetecting time series stationarityGetting readyHow to do it…How it works…There's more…See alsoApplying power transformations Getting readyHow to do it…How it works…There's more...See alsoTesting for autocorrelation in time series dataGetting readyHow to do it…How it works…There's more...See also
Chapter 10: Building Univariate Time Series Models Using Statistical Methods
Technical requirementsPlotting ACF and PACFHow to do it…How it works…There's more...See alsoForecasting univariate time series data with exponential smoothingHow to do it…How it works…There's more…See alsoForecasting univariate time series data with non-seasonal ARIMAGetting readyHow to do it…How it works…There's more…See alsoForecasting univariate time series data with seasonal ARIMAHow to do it…How it works…There's more…See also
Chapter 11: Additional Statistical Modeling Techniques for Time Series
Technical requirementsForecasting time series data using auto_arimaGetting readyHow to do it…How it works…There's more…See alsoForecasting time series data using Facebook ProphetGetting readyHow to do it…How it works…There's more…See alsoForecasting multivariate time series data using VARHow to do it…How it works…There's more…See also...Evaluating vector autoregressive (VAR) modelsHow to do it...How it works...There's more...See also…Forecasting volatility in financial time series data with GARCHGetting readyHow to do it…How it works…There's more...See also
Chapter 12: Forecasting Using Supervised Machine Learning
Technical requirementsUnderstanding supervised machine learningPreparing time series data for supervised learningHow to do it…How it works...There's more...See alsoOne-step forecasting using linear regression models with scikit-learnHow to do it...How it works...There's more...See alsoMulti-step forecasting using linear regression models with scikit-learnHow to do it...How it works...There's more…See alsoForecasting using non-linear models with sktimeHow to do it...How it works...There's more...See alsoOptimizing a forecasting model with hyperparameter tuning Getting readyHow to do it...How it works...There's more...See alsoForecasting with exogenous variables and ensemble learning Getting readyHow to do it...How it works...There's more...See also
Chapter 13: Deep Learning for Time Series Forecasting
Technical requirementsInstalling the deep learning librariesUnderstanding artificial neural networksForecasting with an RNN using KerasHow to do it...How it works...There's more...See alsoForecasting with LSTM using KerasHow to do it...How it works...There's more...See alsoForecasting with a GRU using KerasHow to do it...How it works...There's more...See alsoForecasting with an RNN using PyTorchGetting readyHow to do it...How it works...There's more...See alsoForecasting with LSTM using PyTorchHow to do it...How it works...There's more...See alsoForecasting with a GRU using PyTorchHow to do it...How it works...There's more...See also
Chapter 14: Outlier Detection Using Unsupervised Machine Learning
Technical requirementsDetecting outliers using KNNHow to do it...How it works...There's more...See alsoDetecting outliers using LOFHow to do it...How it works...There's more...See alsoDetecting outliers using iForestHow to do it...How it works...There's more...See alsoDetecting outliers using One-Class Support Vector Machine (OCSVM)How to do it...How it works...There's more...See alsoDetecting outliers using COPODHow to do it...How it works...There's more...See alsoDetecting outliers with PyCaretGetting readyHow to do it...How it works...See also
Chapter 15: Advanced Techniques for Complex Time Series
Technical requirementsUnderstanding state-space modelsDecomposing time series with multiple seasonal patterns using MSTLGetting readyHow to do it...How it works...There's more...See alsoForecasting with multiple seasonal patterns using the Unobserved Components Model (UCM)How to do it...How it works...There's more... See alsoForecasting time series with multiple seasonal patterns using ProphetHow to do it...How it works...There's more...See alsoForecasting time series with multiple seasonal patterns using NeuralProphetGetting readyHow to do it...How it works...There's more...See also
Index
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Overview

Time Series Analysis with Python Cookbook is a comprehensive guide that takes you through real-world recipes for analyzing and forecasting time series data using Python. By progressing through the chapters, you'll learn to clean, visualize, and forecast trends, making this book an invaluable resource for practitioners tackling complex time series datasets.

What this Book will help me do

Prepare and clean time series data using Python, handling issues like missing data and outliers.
Implement statistical models such as ARIMA and SARIMA for time series forecasting.
Apply machine learning and deep learning algorithms to forecast trends and detect anomalies.
Effectively visualize time series data using libraries such as hvPlot for enhanced exploratory data analysis.
Evaluate and optimize your predictive models using robust statistical and computational methods.

Author(s)

Tarek A. Atwan is a renowned data scientist and educator specializing in time series analysis and machine learning. With years of experience applying Python to solve real-world data challenges, Tarek brings practical insights and a passion for teaching. His books and workshops emphasize clarity and hands-on practice to equip readers with actionable skills.

Who is it for?

This book is ideal for data professionals, including data analysts, scientists, and engineers, who use Python to tackle data challenges involving time series. It caters to those with basic Python programming skills and minimal exposure to math and statistics. Readers new to time series or looking for practical recipes to enhance their methodology will find it particularly useful. A basic familiarity with business analytics will also be beneficial.

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Getting Started with Time Series Forecasting in Python

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