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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics FREE CHAPTER 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Auto-regressive moving averages and other time series models

The model that we tried earlier was a relatively simple pure auto-regressive model. However, we are not stuck with using auto-regression or pure auto-regression alone in our time series models. As with other classes of machine learning models covered in this book, there is a whole zoo of time series techniques, and we cannot cover them all here. However, we did want to mention a few notable techniques that you could explore as you follow up on this material.

Auto-regressive models are often combined with models called moving average models. When these are combined, they are often referred to as auto-regressive moving average (ARMA) or auto-regressive integrated moving average (ARIMA) models. The moving average part of ARMA/ARIMA models allows you to capture the effects of things like white noise or other error terms...

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