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Modern Time Series Forecasting with Python : Explore Industry-Ready Time Series Forecasting Using Modern Machine Learning and Deep Learning.

By: Publisher: Birmingham : Packt Publishing Ltd., 2022Edition: 1st editionDescription: xxvi, 525 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 1803246804
  • 9781803246802
Subject(s): Additional physical formats: ebook version :: No titleDDC classification:
  • 519.550285 23
LOC classification:
  • QA280 .M345 2022
Contents:
1. Introducing Time Series -- 2. Acquiring and Processing Time Series Data -- 3. Analyzing and Visualizing Time Series Data -- 4. Setting a Strong Baseline Forecast -- 5. Time Series Forecasting as Regression -- 6. Feature Engineering for Time Series Forecasting -- 7. Target Transformations for Time Series Forecasting -- 8. Forecasting Time Series with Machine Learning Models -- 9. Ensembling and Stacking -- 10. Global Forecasting Models -- 11. Introduction to Deep Learning -- 12. Building Blocks of Deep Learning for Time Series -- 13. Common Modeling Patterns for Time Series -- 14. Attention and Transformers for Time Series -- 15. Strategies for Global Deep Learning Forecasting Models -- 16. Specialized Deep Learning Architectures for Forecasting -- 17. Multi-Step Forecasting -- 18. Evaluating Forecasts - Forecast Metrics -- 19. Evaluating Forecasts - Validation Strategies.
Summary: Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You'll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you'll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you'll be able to build world-class time series forecasting systems and tackle problems in the real world. What you will learn: -Find out how to manipulate and visualize time series data like a pro -Set strong baselines with popular models such as ARIMA -Discover how time series forecasting can be cast as regression -Engineer features for machine learning models for forecasting -Explore the exciting world of ensembling and stacking models -Get to grips with the global forecasting paradigm -Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer -Explore multi-step forecasting and cross-validation strategies Who this book is for: The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.
List(s) this item appears in: 2023 New Titles
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Mesa Lab QA280 .M345 2022 1 Available 50583020015586
Total holds: 0

Includes bibliographical references and index.

1. Introducing Time Series --
2. Acquiring and Processing Time Series Data --
3. Analyzing and Visualizing Time Series Data --
4. Setting a Strong Baseline Forecast --
5. Time Series Forecasting as Regression --
6. Feature Engineering for Time Series Forecasting --
7. Target Transformations for Time Series Forecasting --
8. Forecasting Time Series with Machine Learning Models --
9. Ensembling and Stacking --
10. Global Forecasting Models --
11. Introduction to Deep Learning --
12. Building Blocks of Deep Learning for Time Series --
13. Common Modeling Patterns for Time Series --
14. Attention and Transformers for Time Series --
15. Strategies for Global Deep Learning Forecasting Models --
16. Specialized Deep Learning Architectures for Forecasting --
17. Multi-Step Forecasting --
18. Evaluating Forecasts - Forecast Metrics --
19. Evaluating Forecasts - Validation Strategies.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.

This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You'll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you'll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.

By the end of this book, you'll be able to build world-class time series forecasting systems and tackle problems in the real world.

What you will learn:
-Find out how to manipulate and visualize time series data like a pro
-Set strong baselines with popular models such as ARIMA
-Discover how time series forecasting can be cast as regression
-Engineer features for machine learning models for forecasting
-Explore the exciting world of ensembling and stacking models
-Get to grips with the global forecasting paradigm
-Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer
-Explore multi-step forecasting and cross-validation strategies
Who this book is for:
The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Questions? Email library@ucar.edu.

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