Modern Time Series Forecasting with Python
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ISBN: 9789365893625
eISBN: 9789365894998
Authors: Ravindra Rapaka
Rights: Worldwide
Edition: 2026
Pages: 446
Dimension: 7.5*9.25 Inches
Book Type: Paperback
- Description
- Table of Contents
- About the Authors
Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence.
Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters’ models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka.
By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.
WHAT YOU WILL LEARN
● Diagnose trend and seasonality using Statsmodels stationarity.
● Build ARIMA/SARIMA and smoothing models using Statsmodels.
● Engineer lag, rolling, and calendar-based forecasting features.
● Deploy FastAPI pipelines and monitor Kafka drift.
● Build LSTM and GRU architectures with TensorFlow.
● Backtest, compare, and ensemble models with confidence.
● Deploy, monitor, and retrain forecasting pipelines at scale.
WHO THIS BOOK IS FOR
This book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.
1. Introduction to Time Series Data and Analysis
2. Data Pre-processing and Feature Engineering
3. Exploratory and Statistical Analysis of Time Series
4. Autoregressive Models
5. Moving Average and ARMA Models
6. ARIMA and SARIMA Models
7. Exponential Smoothing Methods
8. Feature-based Machine Learning for Time Series Forecasting
9. Introduction to Deep Learning for Time Series
10. Building and Training LSTM Models for Time Series
11. Advanced Deep Learning Architectures and Multivariate Forecasting
12. Multivariate Time Series Forecasting
13. Model Evaluation, Selection, and Ensembling
14. Forecasting at Scale and Model Deployment
15. Time Series Forecasting in Practice
Ravindra Rapaka is an AI leader and practitioner with over a decade of experience in architecting and operating data-driven systems at scale in production environments. He has led work in predictive analytics, optimization, and deep learning with an emphasis on how to bring models out of experimentation, into reliable production.
His academic background is at the intersection of quantitative finance and analytics, and he has postgraduate degrees in financial engineering, business analytics, and applied finance. That informs his approach to modeling and forecasting: it should be rigorous enough to defend, but also practical enough to be useful.
He is particularly interested in how to connect classical statistical thinking to modern machine learning and deep learning workflows. He has applied that through industry work and research contributions, and in leading and mentoring teams to build forecasting solutions that are both accurate and interpretable, and that teams can operate sustainably.
With Modern Time Series Forecasting with Python, he hopes to help professionals to build deep understanding of the full forecasting pipeline: from understanding time series behavior and preparing data, to building advanced models, and deploying them at scale responsibly.