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

2020-10-11 · Vivek Mahato, Pádraig Cunningham

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.

📄 PDF Abstract BibTeX arXiv:2010.05270

Code (0)

등록된 구현이 없습니다.

Tasks

Dynamic Time WarpingregressionTime SeriesTime Series Analysis Time Series Regression

Methods 이 논문이 사용한 방법론

DTW Dynamic Time Warping (DTW) [1] is one of well-known distance measures between a pairwise of time series. The main idea of DTW is to compute the distance from the matching of…

Exact Mean Computation in Dynamic Time Warping Spaces

2017-10-24 · Markus Brill, Till Fluschnik, Vincent Froese, Brijnesh Jain 외

Dynamic time warping constitutes a major tool for analyzing time series. In particular, computing a mean series of a given sample of series in dynamic time warping spaces (by minimizing the Fr\'echet function) is a chall…

Benchmarking Dynamic Time Warping Time Series Time Series Analysis

Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)

2019-10-22 · Mohamed Djallel Dilmi, Laurent Barthès, Cécile Mallet, Aymeric Chazottes

Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (feature-based) have shown their limitations f…

Clustering Dynamic Time Warping Time Series Time Series Analysis

Comparing Temporal Graphs Using Dynamic Time Warping

2018-10-15 · Vincent Froese, Brijnesh Jain, Rolf Niedermeier, Malte Renken

Within many real-world networks the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to c…

Dynamic Time Warping Time Series Analysis

Assessing the Impact of Anisotropy in Neural Representations of Speech: A Case Study on Keyword Spotting

2025-06-06 · Guillaume Wisniewski, Séverine Guillaume, Clara Rosina Fernández

Pretrained speech representations like wav2vec2 and HuBERT exhibit strong anisotropy, leading to high similarity between random embeddings. While widely observed, the impact of this property on downstream tasks remains u…

Dynamic Time Warping Keyword Spotting

Graphical Time Warping for Joint Alignment of Multiple Curves

2016-12-01 · NeurIPS 2016 12 · Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang 외

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In man…

Dynamic Time Warping Time Series Time Series Analysis