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Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
Learn traditional and cutting-edge Machine Learning (ML) and deep learning techniques and best practices for time series forecasting with Python, including global ML models, conformal prediction, and transformer architectures
Key Features
- Work through examples of how to use machine learning and global machine learning models for forecasting
- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS
- Learn probabilistic forecasting with conformal prediction and quantile regressions
- Purchase of the print or Kindle book includes a free eBook in PDF format
Book Description
Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. With Modern Time Series Forecasting with Python, Second Edition, you'll master cutting-edge deep learning architectures and advanced statistical techniques alongside classic methods like ARIMA and exponential smoothing. Learn the fundamentals from preprocessing, feature engineering, and evaluation to applying powerful machine and deep learning models, including ensemble and global methods.
This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.
What you will learn
- Build machine learning models for regression-based time series forecasting
- Apply powerful feature engineering techniques to enhance prediction accuracy
- Tackle common challenges like non-stationarity and seasonality
- Combine multiple forecasts using ensembling and stacking for superior results
- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series
- Evaluate and validate your forecasts using best practices and statistical metrics
Who this book is for
This book is ideal for data scientists, quantitative analysts, financial analysts, meteorologists, risk analysts, and anyone interested in leveraging Python for accurate time series forecasting.
Table of Contents
- Introducing Time Series
- Acquiring and Processing Time Series Data
- Analyzing and Visualizing Time Series Data
- Setting a Strong Baseline Forecast
- Time Series Forecasting as Regression
- Feature Engineering for Time Series Forecasting
- Target Transformations for Time Series Forecasting
- Forecasting Time Series with Machine Learning Models
- Ensembling and Stacking
- Global Forecasting Models
- Introduction to Deep Learning
- Building Blocks of Deep Learning for Time Series
- Common Modeling Patterns for Time Series
- Attention and Transformers for Time Series
- Strategies for Global Deep Learning Forecasting Models
- Specialized Deep Learning Architectures for Forecasting
- Probabilistic Forecasting and Other Use Cases
- Multi-Step Forecasting
- Evaluating Forecasts – Forecast Metrics
- Evaluating Forecasts – Validation Strategies
- ISBN-101835883184
- ISBN-13978-1835883181
- PublisherPackt Publishing - ebooks Account
- Publication dateOctober 9, 2024
- LanguageEnglish
- Dimensions1.41 x 7.5 x 9.25 inches
- Print length628 pages
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Editorial Reviews
About the Author
Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library—PyTorch Tabular—which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
Jeff Tackes is a seasoned data scientist specializing in demand forecasting with over a decade of industry experience. Currently he is at Kraft Heinz, where he leads the research team in charge of demand forecasting. He has pioneered the development of best-in-class forecasting systems utilized by leading Fortune 500 companies. Jeff's approach combines a robust data-driven methodology with innovative strategies, enhancing forecasting models and business outcomes significantly. Leading cross-functional teams, Jeff has designed and implemented demand forecasting systems that have markedly improved forecast accuracy, inventory optimization, and customer satisfaction. His proficiency in statistical modeling, machine learning, and advanced analytics has led to the implementation of forecasting methodologies that consistently surpass industry norms. Jeff's strategic foresight and his capability to align forecasting initiatives with overarching business objectives have established him as a trusted advisor to senior executives and a prominent expert in the data science domain. Additionally, Jeff actively contributes to the open-source community, notably to PyTimeTK, where he develops tools that enhance time series analysis capabilities. He currently resides in Chicago, IL with his wife and son.
Product details
- ASIN : B0D6G3SHD6
- Publisher : Packt Publishing - ebooks Account (October 9, 2024)
- Language : English
- Paperback : 628 pages
- ISBN-10 : 1835883184
- ISBN-13 : 978-1835883181
- Item Weight : 11.4 ounces
- Dimensions : 1.41 x 7.5 x 9.25 inches
- Best Sellers Rank: #1,273,437 in Books (See Top 100 in Books)
- #149 in Stochastic Modeling
- #263 in Machine Theory (Books)
- #474 in Business Planning & Forecasting (Books)
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