Are you looking for the best books on Time Series Analysis?… If yes, then this article is for you. In this article, you will find the Best Books to Learn Time Series Analysis for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Books to Learn Time Series Analysis for Beginners and find the Best Books to Learn Time Series Analysis for Beginners to Advanced according to your need.
In the previous article, I shared the Best Machine Learning Books for beginners to Advanced to read in 2022, you can go through the list and enjoy reading.
Best Books to Learn Time Series Analysis for Beginners to Advanced to know in 2022
The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty.
The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels.
In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods.
This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
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.
- Understand what makes time series data different from other data
- Apply various imputation and interpolation strategies for missing data
- Implement different models for univariate and multivariate time series
- Use different deep learning libraries such as TensorFlow, Keras, and PyTorch
- 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
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.
Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.
You’ll get the guidance you need to confidently:
- Find and wrangle time series data
- Undertake exploratory time series data analysis
- Store temporal data
- Simulate time series data
- Generate and select features for a time series
- Measure error
- Forecast and classify time series with machine or deep learning
- Evaluate accuracy and performance
The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models.
In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data.
ime Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.
The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.
Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting.
- Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance
- More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data
- New material on frequency domain and spatial temporal data analysis
- Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions
- A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems
Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-level courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
Introducing time series methods and their application in social science research, this practical guide to time series models is the first in the field written for a non-econometrics audience. Giving readers the tools they need to apply models to their own research, Introduction to Time Series Analysis, by Mark Pickup, demonstrates the use of―and the assumptions underlying―common models of time series data including finite distributed lag; autoregressive distributed lag; moving average; differenced data; and GARCH, ARMA, ARIMA, and error correction models.
“This volume does an excellent job of introducing modern time series analysis to social scientists who are already familiar with basic statistics and the general linear model.”
Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series.
This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo.
You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages.
The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You’ll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package.
By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods.
- Visualize time series data and derive better insights
- Explore auto-correlation and master statistical techniques
- Use time series analysis tools from the stats, TSstudio, and forecast packages
- Explore and identify seasonal and correlation patterns
- Work with different time series formats in R
- Explore time series models such as ARIMA, Holt-Winters, and more
- Evaluate high-performance forecasting solutions
Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.
Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:
- A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
- An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
- Numerous examples drawn from finance, economics, engineering, and other related fields
- The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
- Updates to literature references throughout and new end-of-chapter exercises
- Streamlined chapter introductions and revisions that update and enhance the exposition
Multivariate Time Series Analysis: With R and Financial Applications (Wiley Series in Probability and Statistics)
Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research.
Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses.
Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes:
• Over 300 examples and exercises to reinforce the presented content
• User-friendly R subroutines and research presented throughout to demonstrate modern applications
• Numerous datasets and subroutines to provide readers with a deeper understanding of the material
Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
After an overview of how regression works and why to use it, the book covers a range of topics, including specifying and assessing models, practical applications, types of effects, statistical significance, predictions, and an array of problem-solving techniques. Contains practical and analytical guidance.
- Select the correct type of regression analysis.
- Specify the best model and assess how well it fits the data.
- Interpret the results.
- Understand main effects, interaction effects, and modeling curvature.
- Use polynomials, data transformations, and weighted least squares.
- Generate predictions and evaluate their precision.
- Check the assumptions and resolve issues.
- Identify and manage unusual observations.
- Examples of many regression models and scenarios.
- Access free downloadable datasets so you can work the examples yourself.
This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals.
It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists.
Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses.
Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button.
The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter.
Many of the methods presented (including convolution, the Fourier transform, and Euler’s formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python
The pandas library is massive, and it’s common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.
This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operation.
Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
- Master data exploration in pandas through dozens of practice problems
- Group, aggregate, transform, reshape, and filter data
- Merge data from different sources through pandas SQL-like operations
- Create visualizations via pandas hooks to matplotlib and seaborn
- Use pandas, time series functionality to perform powerful analyses
- Import, clean, and prepare real-world datasets for machine learning
- Create workflows for processing big data that doesn’t fit in memory
And here the list ends. So, these are the Best Books to Learn Time Series Analysis for Beginners to Advanced. I will keep adding more Best Books on Time Series Analysis to this list.
I hope these Best Books to Learn Time Series Analysis for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.