You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Product type Paperback

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Length 552 pages

Edition 1st Edition

Languages

Python

Tools

Time Series Forecasting

Concepts

Data Science

Author (1):

Manu Joseph

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Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series FREE CHAPTER

3. Chapter 2: Acquiring and Processing Time Series Data

4. Chapter 3: Analyzing and Visualizing Time Series Data

5. Chapter 4: Setting a Strong Baseline Forecast

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

8. Chapter 6: Feature Engineering for Time Series Forecasting

9. Chapter 7: Target Transformations for Time Series Forecasting

10. Chapter 8: Forecasting Time Series with Machine Learning Models

11. Chapter 9: Ensembling and Stacking

12. Chapter 10: Global Forecasting Models

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

15. Chapter 12: Building Blocks of Deep Learning for Time Series

16. Chapter 13: Common Modeling Patterns for Time Series

17. Chapter 14: Attention and Transformers for Time Series

18. Chapter 15: Strategies for Global Deep Learning Forecasting Models

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

23. Chapter 19: Evaluating Forecasts – Validation Strategies

24. Index

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25. Other Books You May Enjoy

Visualizing time series data

In Chapter 2, Acquiring and Processing Time Series Data, we learned how to prepare a data model as a first step toward analyzing a new dataset. If preparing a data model is like approaching someone you like and making that first contact, then EDA is like dating that person. At this point, you have the dataset, and you are trying to get to know them, trying to figure out what makes them tick, what the person likes and dislikes, and so on.

EDA often employs visualization techniques to uncover patterns, spot anomalies, form and test hypotheses, and so on. Spending some time understanding your dataset will help you a lot when you are trying to squeeze out every last bit of performance from the models. You may understand what sort of features you must create, or what kind of modeling techniques should be applied, and so on.

In this chapter, we will cover a few visualization techniques that are well suited for time series datasets.

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Table of Contents (26) Chapters

Visualizing time series data

Authors (1)

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You're reading from Modern Time Series Forecasting with Python Explore industry-ready time series forecasting using modern machine learning and deep learning

Table of Contents (26) Chapters

Visualizing time series data

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