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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 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 A. Basic Concepts B. 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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