Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain

2019-03-21 · Soheil Khorram, Melvin G McInnis, Emily Mower Provost

DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on 85 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks.

📄 PDF Abstract BibTeX arXiv:1903.09245

Code (1)

soheil-khorram/TTW

Tasks

General ClassificationTime SeriesTime Series AnalysisTime Series Averaging

Aligning Time Series on Incomparable Spaces

2020-06-22 · Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos 외

Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces. In this work, we consider a setting in which time series live on differe…

Dynamic Time Warping Imitation Learning Time Series Time Series Analysis

Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification

2025-07-13 · Jintao Qu, Zichong Wang, Chenhao Wu, Wenbin Zhang arxiv

Neural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-start scenarios. Moreover, they lack int…

Time Series Classification

Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering

2023-06-30 · Ruiyu Xu, Chao Wang, Yongxiang Li, Jianguo Wu

Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temp…

Classification Clustering Dictionary Learning Dynamic Time Warping+2

Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

2018-02-16 · Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira, Matteo Salvatori

In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the…

Dynamic Time Warping Time Series Time Series Analysis

Diffeomorphic Temporal Alignment Nets

2019-12-10 · NeurIPS 2019 12 · Ron Shapira Weber, Matan Eyal, Nicki Skafte, Oren Shriki 외

Time-series analysis is confounded by nonlinear time warping of the data. Traditional methods for joint alignment do not generalize: after aligning a given signal ensemble, they lack a mechanism, that does not require so…

ECG Classification Electrocardiography (ECG)Time Series Time Series Alignment+2