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Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
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Authors (3):
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Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
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Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning FREE CHAPTER 2. Data Cleaning and Pre-processing 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

Time Series Features

Time series data is a special type of data where some quantities are measured over time, and therefore it contains data along with the timestamp. An examples would be stock prices and forecasting of the market, where we would have a stock name, stock value, and time as the time series data.

The following figure presents some time series features:

Figure 3.3: Time series features

The time series features are as follows:

  1. Lag features: Using the lag feature of time series data, we can shift the time series data values by a specific value.
  2. Difference in value of timestamp: In time series data, it is important to derive the difference between timestamps.
  3. Window Features: Window features are features that tend to change over a fixed interval of time (window). These could be measured using the change over time window, growth of measured value with time, or average over time window.
  4. Power/Energy: This features denotes the power consumption...
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