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Machine Learning for Time-Series with Python

You're reading from  Machine Learning for Time-Series with Python

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Ben Auffarth Ben Auffarth
Profile icon Ben Auffarth

Table of Contents (15) Chapters

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python 3. Preprocessing Time-Series 4. Introduction to Machine Learning for Time-Series 5. Forecasting with Moving Averages and Autoregressive Models 6. Unsupervised Methods for Time-Series 7. Machine Learning Models for Time-Series 8. Online Learning for Time-Series 9. Probabilistic Models for Time-Series 10. Deep Learning for Time-Series 11. Reinforcement Learning for Time-Series 12. Multivariate Forecasting 13. Other Books You May Enjoy
14. Index

K-nearest neighbors with dynamic time warping

K-nearest neighbors is a well-known machine learning method (sometimes also going under the guise of case-based reasoning). In kNN, we can use a distance measure to find similar data points. We can then take the known labels of these nearest neighbors as the output and integrate them in some way using a function.

Figure 7.3 illustrates the basic idea of kNN for classification (source – WikiMedia Commons: https://commons.wikimedia.org/wiki/File:KnnClassification.svg):

/Users/ben/Downloads/Machine-Learning for Time-Series with Python/knn.png

Figure 7.3: K-nearest neighbor for classification

We know a few data points already. In the preceding illustration, these points are indicated as squares and triangles, and they represent data points of two different classes, respectively. Given a new data point, indicated by a circle, we find the closest known data points to it. In this example, we find that the new point is similar to triangles, so we might assume that the new point is of the triangle...

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