8 New Time Series Books Reshaping Forecasting in 2025

Drawing from his deep expertise in econometrics, Oliver Linton presents a textbook that bridges traditional time series analysis with modern data science approaches tailored for economics and finance. You’ll explore Bayesian inference, machine learning techniques, and continuous time econometrics, each grounded in rigorous theory and reinforced with practical exercises. For instance, the chapters on nonparametric smoothing provide fresh perspectives that challenge conventional linear methods, helping you grasp complex temporal patterns in financial data. This book suits advanced undergraduates and graduate students who want a thorough, mathematically sound understanding of time series specifically applied to economic and financial contexts.

Drawing from over two decades as a data scientist and adjunct professor, Chris Kuo delivers an extensive exploration of time series forecasting and anomaly detection that bridges classical methods with recent advances like Transformer models. You’ll gain hands-on familiarity with a broad spectrum of techniques—from Prophet and NeuralProphet to tree-based and deep learning algorithms—organized into six focused parts that progressively build your expertise. The book’s unique strength lies in its balanced approach: intuitive explanations precede technical depth, enabling you to grasp both the why and how behind each method. Whether you’re analyzing financial markets or healthcare data, you’ll find practical insights to enhance your predictive modeling and interpretability skills.

This tailored book explores the forefront of AI-driven forecasting methods in time series as they stand in 2025. It examines the latest developments that redefine how temporal data is analyzed and predicted, with a focus on emerging AI techniques that push beyond traditional approaches. By aligning closely with your background and interests, this book delves into cutting-edge models, novel neural architectures, and adaptive algorithms designed to handle complex and high-dimensional time series challenges. The personalized content ensures you engage deeply with the most relevant advancements, making it easier to stay at the leading edge of time series forecasting research and application.

Vitor Cerqueira's deep involvement in time series research and machine learning shines through this practical guide, which arose from his academic and postdoctoral work in forecasting methods. You’ll learn how to harness deep learning techniques like convolutional neural networks and transformers, applying them to real-world time series problems such as forecasting, classification, and anomaly detection. The book walks you through using PyTorch to develop models that handle univariate and multivariate data, blending theory with hands-on coding recipes. If you're comfortable with Python and want to elevate your forecasting skills with advanced architectures, this book matches your ambitions without overcomplicating the material.

After analyzing the challenges faced by natural sciences researchers in handling time series data, Victor Privalsky developed methods that simplify the complex process of time and frequency domain analysis. His approach allows you to focus on understanding the properties of scalar and multivariate time series without getting bogged down by intricate calculations, using autoregressive models selected by multiple criteria. You learn to interpret stochastic difference equations, spectral density estimates, and predictability, alongside tools for Granger causality and coherence functions, all supported by practical examples including engineering cases. This book suits scientists and engineers who want to deepen their grasp of time series analysis with user-friendly software but don’t necessarily have a background in random process theory.

What happens when a distinguished professor of business analytics tackles time series forecasting? Galit Shmueli, with coauthor Julia Polak, offers a focused, applied guide that goes beyond theory into practical use of R software for forecasting. You’ll explore methods ranging from smoothing and ARIMA models to neural networks and ensembles, all tied closely to real business goals. The book’s chapters include exercises and case studies that help you evaluate forecasting performance and apply techniques to operations, marketing, and finance. If you want to move past abstract statistics and develop forecasting skills you can use in business contexts, this book will meet your needs—though pure theorists may find its hands-on approach less appealing.

This tailored book delves into the evolving landscape of time series analysis, revealing emerging trends and cutting-edge discoveries shaping 2025 and beyond. It explores how new temporal data methodologies and innovations can be understood through a personalized lens that matches your background and goals. The content focuses on recent developments in time series forecasting, anomaly detection, and AI-driven modeling, providing a clear path through complex, fast-moving advancements. By tailoring the material to your interests, this book allows you to engage deeply with the newest knowledge, keeping you at the forefront of temporal data research and its practical applications.

This book challenges the traditional methods of time series forecasting by integrating cloud platforms like Azure Time Series Insights and AWS Forecast with Python programming. Shanthababu Pandian, leveraging over two decades in IT and advanced degrees in AI and data science, guides you through essential Python packages and foundational models such as ARIMA and exponential smoothing, then advances to deep learning techniques tailored for time series data. The detailed chapters on vector autoregression and cloud-based forecasting tools offer a practical edge for data professionals aiming to harness scalable, modern analytics. If you’re looking to deepen your understanding of enterprise-level forecasting and deploy solutions in cloud environments, this book provides a focused, skill-driven approach without unnecessary jargon.

Perez's extensive engagement with MATLAB's Deep Learning Toolbox drives this focused exploration of neural networks for time series forecasting. You gain hands-on familiarity with dynamic neural networks tailored to predict future values using both single and multiple time series inputs, including nonlinear autoregressive models and NARX architectures. The book emphasizes practical application through MATLAB's GUI and command-line scripting, making it accessible whether you're experimenting with sample data or tackling your own datasets. If your work hinges on applying neural network techniques to time series data within MATLAB, this text offers clear methodologies and examples to refine your skills.

Raghurami Reddy Etukuru, Ph.D., draws on his deep expertise in data science and artificial intelligence to tackle the intricate challenges of forecasting complex time series data. You’ll explore how non-linearity, non-stationarity, and other complicated patterns influence predictive models, moving beyond traditional statistical approaches. The book offers insights into leveraging AI techniques to decode these patterns, especially in chapters that dissect long memory and stochasticity. If you’re grappling with real-world data that defies simple analysis, this text provides a thoughtful framework to enhance your prediction and decision-making skills, although it’s best suited for those with prior experience in time series analysis rather than beginners.