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Comet for Data Science

You're reading from   Comet for Data Science Enhance your ability to manage and optimize the life cycle of your data science project

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801814430
Length 402 pages
Edition 1st Edition
Tools
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Author (1):
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Angelica Lo Duca Angelica Lo Duca
Author Profile Icon Angelica Lo Duca
Angelica Lo Duca
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1 – Getting Started with Comet
2. Chapter 1: An Overview of Comet FREE CHAPTER 3. Chapter 2: Exploratory Data Analysis in Comet 4. Chapter 3: Model Evaluation in Comet 5. Section 2 – A Deep Dive into Comet
6. Chapter 4: Workspaces, Projects, Experiments, and Models 7. Chapter 5: Building a Narrative in Comet 8. Chapter 6: Integrating Comet into DevOps 9. Chapter 7: Extending the GitLab DevOps Platform with Comet 10. Section 3 – Examples and Use Cases
11. Chapter 8: Comet for Machine Learning 12. Chapter 9: Comet for Natural Language Processing 13. Chapter 10: Comet for Deep Learning 14. Chapter 11: Comet for Time Series Analysis 15. Other Books You May Enjoy

Introducing basic concepts related to time series analysis

A time series is an ordered sequence of values over time, representing the variation of a certain phenomenon. Examples of time series include the trend of the prices of a certain product, and the trend of rainfall in a given region over time. The following figure shows an example of time series representing the natural gas price from 2000 to 2020:

Figure 11.1 – The natural gas price time series

Data was extracted from the DataHub website and is available at https://datahub.io/core/natural-gas under the public domain and the use of Energy Information Administration (EIA) content license.

Time series analysis, also known as time series forecasting, is the study of the past values of a time series, with the purpose of building a model that predicts its future values.

In this section, you will learn the following basic concepts and aspects related to time series:

  • Loading a time series...
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