What this book covers
Chapter 1, Getting Started with Time Series Analysis, is a general introduction to Python development best practices. You will learn different techniques to create and manage virtual environments, install and manage Python packages, manage dependencies, and finally, how to install and manage Jupyter extensions.
Chapter 2, Reading Time Series Data from Files, is an introduction to time series data. This chapter shows you how to read data from various and commonly used file types, whether stored locally or on the cloud. The recipes will highlight advanced options for ingesting, preparing, and transforming data into a time series DataFrame for later analysis.
Chapter 3, Reading Time Series Data from Databases, picks up from Chapter 2, Reading Time Series Data from Files, and focuses on reading data from various database systems, including relational (PostgreSQL and MySQL) and non-relational (MongoDB and InfluxDB), whether on-premises or a cloud service (Amazon Redshift and Snowflake). The recipes will highlight different methods and techniques to offer flexibility on how data can be ingested, prepared, and transformed into a time series DataFrame for later analysis.
Chapter 4, Persisting Time Series Data to Files, covers different options and use cases to store time series data for later retrieval. The techniques will cover various methods and file types, whether on-premises or in the cloud. In addition, this chapter covers serialization, compression, overwriting, or appending to files.
Chapter 5, Persisting Time Series Data to Databases, builds on Chapter 4, Persisting Time Series Data to Files, focusing on writing data for scale. This covers different techniques for writing data to relational and non-relational database systems like those discussed in Chapter 3, Reading Time Series Data from Databases, including on-premises and cloud services.
Chapter 6, Working with Date and Time in Python, takes a practical and intuitive approach to an intimidating topic. You will learn how to deal with the complexity of dates and time in your time series data. The chapter illustrates practical use cases for handling time zones, custom holidays, and business days, working with Unix epoch and UTC. Typically, this intimidating topic is presented in a fun and practical way that you will find helpful to apply right away.
Chapter 7, Handling Missing Data, explores different methods for identifying and handling missing data. You will learn different imputation and interpolation techniques. The chapter starts with simple statistical methods for univariate imputation and then explores various univariate interpolation algorithms for more advanced multivariate imputation.
Chapter 8, Outlier Detection Using Statistical Methods, covers statistical methods for outlier and anomaly detection. These practical yet straightforward techniques are easy to interpret and implement. The chapter uses data from the Numenta Anomaly Benchmark (NAB) to evaluate different anomaly detection algorithms.
Chapter 9, Exploratory Data Analysis and Diagnosis, dives into visualization techniques for effective Exploratory Data Analysis (EDA) with interactive visualizations. You will learn how to investigate and diagnose your time series data to test for specific assumptions such as stationarity and autocorrelation. Finally, the chapter covers practical recipes for transforming your time series data using a family of power transforms, decomposition, and differencing methods.
Chapter 10, Building Univariate Time Series Models Using Statistical Methods, kick offs the journey into modeling and forecasting time series. The chapter intuitively explains what autocorrelation function (ACF) and partial autocorrelation function (PACF) plots are and how they are used, and then moves in to training, diagnosing, and comparing different models, including exponential smoothing, autoregressive integrated moving average (ARIMA), and seasonal ARIMA (SARIMA). Additionally, this chapter introduces grid search and hyperparameter tuning.
Chapter 11, Additional Statistical Modeling Techniques for Time Series, picks up from Chapter 10, Building Univariate Time Series Models Using Statistical Methods, diving into more advanced and practical models, such as vector autoregressive (VAR) for multivariate time series, generalized autoregressive conditional heteroskedasticity (GARCH) for forecasting volatility, and an introduction to the Prophet algorithm and library.
Chapter 12, Forecasting Using Supervised Machine Learning, will take you from classical time series forecasting techniques to more advanced machine learning algorithms. The chapter shows how time series data can be transformed appropriately to be suitable for supervised machine learning. In addition, you will explore a variety of machine learning algorithms and implement multi-step forecasting, using both scikit-learn and sktime.
Chapter 13, Deep Learning for Time Series Forecasting, covers more advanced deep learning architectures using TensorFlow/Keras and PyTorch. The chapter starts with a high-level API (Keras) and then dives into more complex implementations, using a lower-level API (PyTorch).
Chapter 14, Outlier Detection Using Unsupervised Machine Learning, continues from Chapter 8, Outlier Detection Using Statistical Methods, but focuses on more advanced unsupervised machine learning methods. You will use the same datasets from the NAB to allow you to compare statistical and machine learning techniques using the same benchmark data. The techniques cover a variety of machine learning algorithms.
Chapter 15, Advanced Techniques for Complex Time Series, will introduce more complex time series data that contains multiple seasonal patterns. The chapter includes how such time series data can be decomposed and explores different modeling techniques, including state-space models.
