Book Image

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
Book Image

Time Series Analysis with Python Cookbook

By: Tarek A. Atwan

Overview of this book

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch. Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Conventions used
Sections
Get in touch
Share Your Thoughts
1
Chapter 1: Getting Started with Time Series Analysis
Chapter 1: Getting Started with Time Series Analysis
Technical requirements
Development environment setup
Installing Python libraries
Installing JupyterLab and JupyterLab extensions
Free Chapter
2
Chapter 2: Reading Time Series Data from Files
Chapter 2: Reading Time Series Data from Files
Technical requirements
Reading data from CSVs and other delimited files
Reading data from an Excel file
Reading data from URLs
Reading data from a SAS dataset
3
Chapter 3: Reading Time Series Data from Databases
Chapter 3: Reading Time Series Data from Databases
Technical requirements
Reading data from a relational database
Reading data from Snowflake
Reading data from a document database (MongoDB)
Reading third-party financial data using APIs
Reading data from a time series database (InfluxDB)
4
Chapter 4: Persisting Time Series Data to Files
Chapter 4: Persisting Time Series Data to Files
Technical requirements
Serializing time series data with pickle
Writing to CSV and other delimited files
Writing data to an Excel file
Storing data to S3
5
Chapter 5: Persisting Time Series Data to Databases
Chapter 5: Persisting Time Series Data to Databases
Technical requirements
Writing time series data to a relational database (PostgreSQL and MySQL)
Writing time series data to MongoDB
Writing time series data to InfluxDB
Writing time series data to Snowflake
6
Chapter 6: Working with Date and Time in Python
Chapter 6: Working with Date and Time in Python
Technical requirements
Working with DatetimeIndex
Providing a format argument to DateTime
Working with Unix epoch timestamps
Working with time deltas
Converting DateTime with time zone information
Working with date offsets
Working with custom business days
7
Chapter 7: Handling Missing Data
Chapter 7: Handling Missing Data
Technical requirements
Understanding missing data
Performing data quality checks
Handling missing data with univariate imputation using pandas
Handling missing data with univariate imputation using scikit-learn
Handling missing data with multivariate imputation
Handling missing data with interpolation
8
Chapter 8: Outlier Detection Using Statistical Methods
Chapter 8: Outlier Detection Using Statistical Methods
Technical requirements
Understanding outliers
Resampling time series data
Detecting outliers using visualizations
Detecting outliers using the Tukey method
Detecting outliers using a z-score
Detecting outliers using a modified z-score
9
Chapter 9: Exploratory Data Analysis and Diagnosis
Chapter 9: Exploratory Data Analysis and Diagnosis
Technical requirements
Plotting time series data using pandas
Plotting time series data with interactive visualizations using hvPlot
Decomposing time series data
Detecting time series stationarity
Applying power transformations
Testing for autocorrelation in time series data
10
Chapter 10: Building Univariate Time Series Models Using Statistical Methods
Chapter 10: Building Univariate Time Series Models Using Statistical Methods
Technical requirements
Plotting ACF and PACF
Forecasting univariate time series data with exponential smoothing
Forecasting univariate time series data with non-seasonal ARIMA
Forecasting univariate time series data with seasonal ARIMA
11
Chapter 11: Additional Statistical Modeling Techniques for Time Series
Chapter 11: Additional Statistical Modeling Techniques for Time Series
Technical requirements
Forecasting time series data using auto_arima
Forecasting time series data using Facebook Prophet
Forecasting multivariate time series data using VAR
Evaluating vector autoregressive (VAR) models
Forecasting volatility in financial time series data with GARCH
12
Chapter 12: Forecasting Using Supervised Machine Learning
Chapter 12: Forecasting Using Supervised Machine Learning
Technical requirements
Understanding supervised machine learning
Preparing time series data for supervised learning
One-step forecasting using linear regression models with scikit-learn
Multi-step forecasting using linear regression models with scikit-learn
Forecasting using non-linear models with sktime
Optimizing a forecasting model with hyperparameter tuning
Forecasting with exogenous variables and ensemble learning
13
Chapter 13: Deep Learning for Time Series Forecasting
Chapter 13: Deep Learning for Time Series Forecasting
Technical requirements
Understanding artificial neural networks
Forecasting with an RNN using Keras
Forecasting with LSTM using Keras
Forecasting with a GRU using Keras
Forecasting with an RNN using PyTorch
Forecasting with LSTM using PyTorch
Forecasting with a GRU using PyTorch
14
Chapter 14: Outlier Detection Using Unsupervised Machine Learning
Chapter 14: Outlier Detection Using Unsupervised Machine Learning
Technical requirements
Detecting outliers using KNN
Detecting outliers using LOF
Detecting outliers using iForest
Detecting outliers using One-Class Support Vector Machine (OCSVM)
Detecting outliers using COPOD
Detecting outliers with PyCaret
15
Chapter 15: Advanced Techniques for Complex Time Series
Chapter 15: Advanced Techniques for Complex Time Series
Technical requirements
Understanding state-space models
Decomposing time series with multiple seasonal patterns using MSTL
Forecasting with multiple seasonal patterns using the Unobserved Components Model (UCM)
Forecasting time series with multiple seasonal patterns using Prophet
Forecasting time series with multiple seasonal patterns using NeuralProphet
16
Index
Index
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17
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Preface

A familiar association people make is between forecasting and financial data. In reality, forecasting is used in many industries, leveraging historical data to make future predictions. More specifically, this book is about time series analysis, a process to gain better insight from historical data, capture trends and cyclical patterns, and build a suitable forecasting model.

When working with data that contains observations that change over time and is recorded at specific intervals, you are dealing with time series data. You will find time series data in many domains, and the discipline of time series analysis covers various use cases. For example, time series analysis is used in science (forecasting weather, earthquakes, air quality, or species growth), finance (forecasting stock return, budget, sales, or volatility), government (forecasting inflation, unemployment rates, GDP, or population birth rate), medical (tracking infectious disease transmission, monitoring electrocardiogram or blood glucose, or forecasting healthcare costs), engineering (predictive maintenance, production decline analysis, or traffic volume forecasting), business (inventory management, product demand planning, resource planning), and much more. Pretty much, time series data is all around us, and you will most definitely be encountering such data.

By picking this book, you are looking for practical recipes that you can apply and use – less on theory and more on the practical. The book will take you through the complete journey of time series analysis, covering the end-to-end process, including acquiring and ingesting various types of time series data, exploring the data, transforming and manipulating the data, and training models to use in forecasting.

The book covers concepts, techniques, and algorithms commonly used and more advanced and recent approaches with practical use. For example, you will learn to train and validate different models covering statistical methods, machine learning algorithms, and various deep learning architectures for forecasting and outlier (or anomaly) detection. Most importantly, the variety of datasets used in this book will give you a better insight into how these different models work and how you can pick the most appropriate approach to solve your specific problem.

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