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Modern Graph Theory Algorithms with Python

You're reading from   Modern Graph Theory Algorithms with Python Harness the power of graph algorithms and real-world network applications using Python

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781805127895
Length 290 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Franck Kalala Mutombo Franck Kalala Mutombo
Author Profile Icon Franck Kalala Mutombo
Franck Kalala Mutombo
Colleen M. Farrelly Colleen M. Farrelly
Author Profile Icon Colleen M. Farrelly
Colleen M. Farrelly
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Table of Contents (21) Chapters Close

Preface 1. Part 1:Introduction to Graphs and Networks with Examples FREE CHAPTER
2. Chapter 1: What is a Network? 3. Chapter 2: Wrangling Data into Networks with NetworkX and igraph 4. Part 2: Spatial Data Applications
5. Chapter 3: Demographic Data 6. Chapter 4: Transportation Data 7. Chapter 5: Ecological Data 8. Part 3: Temporal Data Applications
9. Chapter 6: Stock Market Data 10. Chapter 7: Goods Prices/Sales Data 11. Chapter 8: Dynamic Social Networks 12. Part 4: Advanced Applications
13. Chapter 9: Machine Learning for Networks 14. Chapter 10: Pathway Mining 15. Chapter 11: Mapping Language Families – an Ontological Approach 16. Chapter 12: Graph Databases 17. Chapter 13: Putting It All Together 18. Chapter 14: New Frontiers 19. Index 20. Other Books You May Enjoy

Introduction to temporal data

In Chapter 2, we briefly introduced temporal data or data in the form of a time series or a group of time series. Time series data tracks important metrics in many different industries: daily store sales volumes, weekly software product marketing lead volumes, daily incidence of an emerging disease, yearly behavior rates (such as smoking or vegetable consumption) in a population, or hourly stock prices tracking market trends. Many related factors can influence trends over time, and some models consider these factors directly if they are known in advance.

However, consider the case of sales trends for a new gem store in a city where gem stores are a new phenomenon, perhaps somewhere rural between Haifa and Tel Aviv (Figure 6.1). Thus, there is very little known about what might influence sales. Understanding what trends exist in the time series data is critical when mining for factors that might influence sales. However, time series datasets pose significant...

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