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Scala for Machine Learning, Second Edition - Second Edition

You're reading from  Scala for Machine Learning, Second Edition - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Pages 740 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (27) Chapters close

Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib Basic Concepts References Index

Moving averages


Moving averages provides data analysts and scientists with a basic predictive model. Despite its simplicity, the moving average method is widely used in a variety of fields such as marketing survey, consumer behavior, or sport statistics. Traders use the moving averages to identify levels of support and resistance for the price of a given security.

Note

Averaging reducing function:

Let's consider a time series xt = x(t) and a function f(xt-p-1,… xt) that reduces the last p observations into a value or average. The estimation of the observation at t is defined by the following formula:

Here, f is an average reducing function from the previous p data points.

Simple moving average

Simple moving average is the simplest form of the moving averaging algorithms [3:2]. The simple moving average of period p estimates the value at time t by computing the average value of the previous p observations using the following formula:

Note

Simple moving average:

M1: The simple moving average of...

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