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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Conventions used

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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

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

Index

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Technical requirements

Throughout this chapter, you will be using the same datasets and functions used in Chapter 12, Forecasting Using Supervised Machine Learning. The handle_missing_data and one_step_forecast functions will remain the same.

The Standardize class will be modified slightly to include a split_data method that splits a dataset into train, validation, and test sets. The validation set is used to evaluate the model's performance at each epoch. The following is the updated code for the Standardize class that you will be using throughout this chapter:

Start by loading the datasets and preprocessing the time series to be suitable for supervised learning. These are the same steps you followed in Chapter 12, Forecasting Using Supervised Machine Learning:
```
Class Standardize:
    def __init__(self, df, split=0.10):
        self.data = df
        self.split = split...
```