A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression

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Title: A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression
Authors: Mahato, VivekCunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/12281
Date: 14-Sep-2018
Online since: 2021-06-22T15:16:01Z
Abstract: It is well understood that Dynamic Time Warping (DTW) is effective in revealing similarities between time series that do not align perfectly. In this paper, we illustrate this on spectroscopy time-series data. We show that DTW is effective in improving accuracy on a regression task when only a single wavelength is considered. When combined with k-Nearest Neighbour, DTW has the added advantage that it can reveal similarities and differences between samples at the level of the time-series. However, in the problem, we consider here data is available across a spectrum of wavelengths. If aggregate statistics (means, variances) are used across many wavelengths the benefits of DTW are no longer apparent. We present this as another example of a situation where big data trumps sophisticated models in Machine Learning.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Conference Publication
Keywords: RegressionTime series dataDynamic time warping
Other versions: https://project.inria.fr/aaldt18/
Language: en
Status of Item: Peer reviewed
Conference Details: The 3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Dublin, Ireland, 10-14 September 2018
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
I-Form Research Collection

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