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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

Setting a Strong Baseline Forecast

In the previous chapter, we saw some techniques we can use to understand time series data, do some Exploratory Data Analysis (EDA), and so on. But now, let’s get to the crux of the matter – time series forecasting. The point of understanding the dataset and looking at patterns, seasonality, and so on was to make the job of forecasting that series easier. And with any machine learning exercise, one of the first things we need to establish before going further is a baseline.

A baseline is a simple model that provides reasonable results without requiring a lot of time to come up with them. Many people think of baselines as something that is derived from common sense, such as an average or some rule of thumb. But as a best practice, a baseline can be as sophisticated as we want it to be, so long as it is quickly and easily implemented. Any further progress we want to make will be in terms of the performance of this baseline.

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