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

• URL: http://arxiv.org/abs/2010.05270v1
• Date: Sun, 11 Oct 2020 15:21:21 GMT
• Title: A Case-Study on the Impact of Dynamic Time Warping in Time Series Regression
• Authors: Vivek Mahato, P\'adraig Cunningham
• Abstract summary: We show that Dynamic Time Warping (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.
• Score: 2.639737913330821
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Evaluating DTW Measures via a Synthesis Framework for Time-Series Data [3.4437947384641037]
  Time-series data originate from various applications that describe specific observations or quantities of interest over time. Dynamic Time Warping (DTW) is the standard approach to achieve an optimal alignment between two temporal signals. Most DTW measures perform well on certain types of time-series data without a clear explanation of the reason. This is the first time such a guideline is presented for selecting a proper DTW measure.
  arXiv  Detail & Related papers  (2024-02-14T05:08:47Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Deep Attentive Time Warping [22.411355064531143]
  We propose a neural network model for task-adaptive time warping. We use the attention model, called the bipartite attention model, to develop an explicit time warping mechanism. Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series.
  arXiv  Detail & Related papers  (2023-09-13T04:49:49Z)
• Approximating DTW with a convolutional neural network on EEG data [9.409281517596396]
  We propose a fast and differentiable approximation of Dynamic Time Wrapping (DTW) We show that our methods achieve at least the same level of accuracy as other DTW main approximations with higher computational efficiency.
  arXiv  Detail & Related papers  (2023-01-30T13:27:47Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection [16.223509730658513]
  We propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimize the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection.
  arXiv  Detail & Related papers  (2022-06-07T00:49:16Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• Elastic Similarity Measures for Multivariate Time Series Classification [4.5669999076671655]
  Elastic similarity measures are a class of similarity measures specifically designed to work with time series data. Elastic similarity measures are widely used in machine learning tasks such as classification, clustering and outlier detection.
  arXiv  Detail & Related papers  (2021-02-20T02:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.