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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st 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 (15) Chapters Close

Preface 1. Getting Started 2. Hello World! FREE CHAPTER 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

Moving averages

Moving averages provide data analysts and scientists with a basic predictive model. Despite its simplicity, the moving average method is widely used in the technical analysis of financial markets to define a dynamic level of support and resistance for the price of a given security.

Note

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

Moving averages

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

The simple moving average

Simple moving average, a smoothing method, is the simplest form of the moving averages algorithms [3:1]. 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

The simple moving average of a time series {xt} with a period p is computed as the average of the last p observations...

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