8 Time Series Books That Separate Experts from Amateurs

Nicolas Vandeput, a supply chain data scientist and educator, wrote this book to bridge the gap between data science theory and practical forecasting in supply chains. You’ll explore how to apply both traditional statistical models and machine learning techniques, with clear Python and Excel examples guiding you through concepts like forecast value added, feature optimization, and handling outliers. The book’s structure, divided into foundational models, advanced machine learning, and process management, equips demand planners and analysts to improve forecasting accuracy and decision-making. If you’re deeply involved in supply chain forecasting and want a hands-on, scientifically grounded approach, this book offers a solid path forward, though it’s best suited to those comfortable with data analysis tools.

Galit Shmueli's long-standing expertise in business analytics and data mining shines through in this book, which takes you beyond theory into applied time series forecasting using R. You’ll explore a range of methods from smoothing algorithms and ARIMA models to neural networks and deep learning, all framed within business contexts that show you how forecasts align with real goals. Chapters include practical cases and exercises that deepen your understanding, like evaluating forecast performance or integrating models with operational data. If you want to handle time series data confidently, especially in business or analytics roles, this book walks you through the technical and strategic sides without unnecessary jargon.

This tailored book explores the intricate world of time series analysis and forecasting, crafted to match your unique background and goals. It covers fundamental concepts like trend detection, seasonality, and autocorrelation, while diving into advanced topics such as ARIMA, state-space models, and machine learning applications. By focusing on your specific interests, it unpacks complex statistical and computational techniques with clarity and enthusiasm. This personalized guide reveals how to interpret data patterns, select appropriate models, and assess forecasting accuracy, ensuring a focused learning journey that bridges expert knowledge with your individual needs.

Peter J. Brockwell and Richard A. Davis bring decades of statistical expertise to this book, designed to build your practical skills in time series analysis and forecasting. You’ll learn to apply ARMA and ARIMA models, explore multivariate time series, and understand state-space models, with chapters dedicated to financial time series and advanced topics like Itô calculus. The inclusion of ITSM2000 software guidance helps you implement concepts directly, making abstract theory accessible. This book suits anyone with basic calculus and statistics knowledge aiming to deepen their analytical toolkit in economics, engineering, or social sciences.

Robert H. Shumway, a distinguished statistician and Fellow of the American Statistical Association, brings deep expertise to this thorough exploration of time series methods. You’ll gain insight into both time and frequency domain techniques, enhanced by real-world examples like MRI pain experiments and nuclear treaty monitoring. The book carefully separates theory from methodology, making it accessible whether you're a graduate student or an advanced undergraduate in physical or social sciences. Chapters cover a wide range of topics—from traditional ARIMA models to nonlinear approaches and modern computational methods such as Monte Carlo Markov chains—giving you a solid toolkit for analyzing complex temporal data.

Drawing from over 16 years of international consulting in data science and machine learning, Tarek A. Atwan crafted this book to tackle the complexities of time series analysis with Python. You’ll learn how to handle everything from data ingestion across various sources to nuanced techniques for detecting anomalies and forecasting using statistical, machine learning, and deep learning models. Chapters provide hands-on Python code for exploratory data analysis, managing missing values, and working with time zones, making it especially useful if you want to strengthen your technical skills in time series forecasting. This book suits data analysts and scientists eager to deepen their practical understanding beyond theory.

This tailored book explores focused actions to boost your forecasting accuracy within a concise 30-day period. It delves into key forecasting principles and presents them through a lens that matches your background and specific goals. By synthesizing a wide array of expert knowledge, it examines practical techniques and common pitfalls, guiding you step-by-step to enhance your predictive skills. This personalized approach ensures that the content aligns with your unique interests, enabling a more efficient learning experience. Whether you're refining existing methods or building new skills, this book reveals a clear path to accelerate your forecasting capabilities, making complex concepts more approachable and immediately applicable.

Drawing from his expertise in data science and machine learning, Manu Joseph offers a detailed exploration of time series forecasting that moves well beyond traditional ARIMA models. You’ll learn to handle, visualize, and engineer features for time series data, applying machine learning and deep learning models like gradient-boosted trees, LSTMs, and transformers on real-world datasets. The book tackles advanced topics such as global forecasting models and cross-validation strategies, making it particularly useful if you want to scale forecasting systems across millions of series. If you're comfortable with Python and eager to push past classical methods into modern, industry-ready forecasting techniques, this book will serve you well.

Rami Krispin’s expertise as a Silicon Valley data scientist and R package developer informs this focused guide on time series analysis using R. The book equips you with concrete skills in handling time series data—from preprocessing with packages like lubridate and xts to applying classical forecasting models such as ARIMA and Holt-Winters. It also ventures into machine learning approaches, including Random Forest and Gradient Boosting with the h2o package, providing a balanced perspective between traditional statistics and modern algorithms. This book suits data analysts and scientists eager to deepen their practical R skills specifically for time series forecasting.

What if everything you thought you knew about analyzing brain signals was challenged? Mike X Cohen, an assistant professor at the Donders Institute, wrote this book to bridge the gap between complex mathematics and practical application in neural data analysis. You’ll explore detailed methods for interpreting MEG, EEG, and LFP recordings, including hands-on Matlab implementations that demystify fundamental concepts like the Fourier transform and signal synchronization. This book suits cognitive scientists, neuroscientists, and psychologists who want to move beyond black-box software and truly understand the intricacies of neural time series analysis.