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Modern Time Series Forecasting with Python

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

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781803246802
Length 552 pages
Edition 1st Edition
Languages
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Author (1):
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Manu Joseph Manu Joseph
Author Profile Icon Manu Joseph
Manu Joseph
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Table of Contents (26) Chapters Close

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 25. Other Books You May Enjoy

Multi-Step Forecasting

In the previous parts, we covered some basics of forecasting and different types of modeling techniques for time series forecasting. But a complete forecasting system is not just the model. There are a few mechanics of time series forecasting that make a lot of difference. These topics cannot be called basics because they require a nuanced understanding of the forecasting paradigm, and that is why we didn’t cover these upfront.

Now that you have worked on some forecasting models and are familiar with time series, it’s time to get more nuanced in our approach. Most of the forecasting exercises we have done throughout the book focus on forecasting the next timestep.

In this chapter, we will look at strategies to generate multi-step forecasting. In other words, how to forecast the next timesteps.

In this chapter, we will be covering these main topics:

  • Why multi-step forecasting?
  • Recursive strategy
  • Direct strategy
  • Joint...
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