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Modern Time Series Forecasting with Python
Modern Time Series Forecasting with Python

Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning

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Modern Time Series Forecasting with Python

Introducing Time Series

Welcome to Advanced Time Series Analysis Using Python! This book is intended for data scientists or machine learning (ML) engineers who want to level up their time series analysis skills by learning new and advanced techniques from the ML world. Time series analysis is something that is commonly overlooked in regular ML books, courses, and so on. They typically start with classification, touch upon regression, and then move on. But it is also something that is immensely valuable and ubiquitous in business. As long as time is one of the four dimensions in the world we live in, time series data is all-pervasive.

Analyzing time series data unlocks a lot of value for a business. Time series analysis isn't new—it's been around since the 1920s and 1930s. But in the current age of data, the time series that are collected by businesses are growing larger and wider by the minute. Combined with an explosion in the quantum of data collected and the renewed interest in ML, the landscape of time series analysis also changed considerably. This book attempts to take you beyond classical statistical methods such as AutoRegressive Integrated Moving Average (ARIMA) and introduce to you the latest techniques from the ML world in time series analysis.

We are going to start with the basics and quickly scale up to more complex topics. In this chapter, we're going to cover the following main topics:

  • What is a time series?
  • Data-generating process (DGP)
  • What can we forecast?
  • Forecasting terminology and notation
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Key benefits

  • Explore industry-tested machine learning techniques used to forecast millions of time series
  • Get started with the revolutionary paradigm of global forecasting models
  • Get to grips with new concepts by applying them to real-world datasets of energy forecasting

Description

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.

Who is this book 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.

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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 24, 2022
Length: 552 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246802
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Product Details

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Length: 552 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246802
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Concepts :

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Frequently bought together


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Total $ 159.97
Time Series Analysis with Python Cookbook
$51.99
Modern Time Series Forecasting with Python
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Table of Contents

25 Chapters
Part 1 – Getting Familiar with Time Series Chevron down icon Chevron up icon
Chapter 1: Introducing Time Series Chevron down icon Chevron up icon
Chapter 2: Acquiring and Processing Time Series Data Chevron down icon Chevron up icon
Chapter 3: Analyzing and Visualizing Time Series Data Chevron down icon Chevron up icon
Chapter 4: Setting a Strong Baseline Forecast Chevron down icon Chevron up icon
Part 2 – Machine Learning for Time Series Chevron down icon Chevron up icon
Chapter 5: Time Series Forecasting as Regression Chevron down icon Chevron up icon
Chapter 6: Feature Engineering for Time Series Forecasting Chevron down icon Chevron up icon
Chapter 7: Target Transformations for Time Series Forecasting Chevron down icon Chevron up icon
Chapter 8: Forecasting Time Series with Machine Learning Models Chevron down icon Chevron up icon
Chapter 9: Ensembling and Stacking Chevron down icon Chevron up icon
Chapter 10: Global Forecasting Models Chevron down icon Chevron up icon
Part 3 – Deep Learning for Time Series Chevron down icon Chevron up icon
Chapter 11: Introduction to Deep Learning Chevron down icon Chevron up icon
Chapter 12: Building Blocks of Deep Learning for Time Series Chevron down icon Chevron up icon
Chapter 13: Common Modeling Patterns for Time Series Chevron down icon Chevron up icon
Chapter 14: Attention and Transformers for Time Series Chevron down icon Chevron up icon
Chapter 15: Strategies for Global Deep Learning Forecasting Models Chevron down icon Chevron up icon
Chapter 16: Specialized Deep Learning Architectures for Forecasting Chevron down icon Chevron up icon
Part 4 – Mechanics of Forecasting Chevron down icon Chevron up icon
Chapter 17: Multi-Step Forecasting Chevron down icon Chevron up icon
Chapter 18: Evaluating Forecasts – Forecast Metrics Chevron down icon Chevron up icon
Chapter 19: Evaluating Forecasts – Validation Strategies Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(30 Ratings)
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4 star 10%
3 star 0%
2 star 10%
1 star 10%
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Machiel Kruger Feb 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Archana Dec 13, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book explores everything from basics of time series to how it can be implemented in using univariate, multivariate and deeplearning algorithms along with the transformations required for implementation.It mainly helps us in the thought process of forecasting and how it can be done efficiently .thanks a lot for the simplistic and elegant explanation.must read for time series enthusiasts.
Amazon Verified review Amazon
Kumar A. Dec 02, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It covers all the aspects of modern time series forecasting. The worked examples are easy to understand and explains the concepts well.This book will help practitioners who will be working with real world time series forecasting problems. Candidates will also find it useful for interview preparations in this field.
Amazon Verified review Amazon
Antra Tripathi Dec 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a one-stop for holistic learning on time series. Very well-articulated in a simple yet engaging manner. For data enthusiasts who are beginning their career in machine learning, this book is an absolute read as it covers all the essential and key aspects to build up your expertise on time series ground up. For seasoned professionals, it provides a guide to further incorporate deep learning strategies to make your time series modelling better.Key aspects of the book:1.) I really like how the book is been compartmentalized into four parts, beginning from a simple introduction to time series to using deep learning architecture for the same.2.) The fact that the content for each sub-sections/ sub-headings in the chapters are supported with mathematical representations, graphs, flow diagrams, tables, pictures, and code snippets makes it engaging and interesting to follow through.3.) Each chapter has an exhaustive list of references and a bonus reading section which lists research papers, journals, and articles from curated repositories like ACM, MIT, Stanford, IEEE, etc.4.) The book also covers modelling strategy using the open-source library 'Py Torch Forecasting ' in a self-containing and self-explanatory way, supported with detailed explanations and documented code snippets. So even if you are new to using Py Torch, it won't be difficult to follow the content of the book.As a data science professional, who recently finished a master's in machine learning, I found this book really useful for both professional and academic purposes. I really enjoyed reading specialized deep learning architecture for forecasting such as Neural Basis Expansion for Interpretable TS Forecasting (N-BEATSx) and Temporal Fusion Transformers (TFT) followed by Mechanics of multistep forecasting and validation strategies. I definitely see myself implementing these learnings in my work.KUDOS to the author Manu for such excellent encapsulation of the topic with his domain expertise! Happy Forecasting!
Amazon Verified review Amazon
Faris Jul 06, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Well written, explainations are detailed enough to get you where you need to go.
Amazon Verified review Amazon
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