# Deep Time Warping for Multiple Time Series Alignment

arXiv: https://arxiv.org/abs/2502.16324 · HTML: https://mlsanity.com/paper/2502.16324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Learning by Aligning Videos in Time](https://mlsanity.com/paper/2103.17260/) (2021)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping](https://mlsanity.com/paper/1906.05947/) (2019)
- [NeuralWarp: Time-Series Similarity with Warping Networks](https://mlsanity.com/paper/1812.08306/) (2018)
- [Neural Time Warping For Multiple Sequence Alignment](https://mlsanity.com/paper/2006.15753/) (2020)
- [DTW-Merge: A Novel Data Augmentation Technique for Time Series Classification](https://mlsanity.com/paper/2103.01119/) (2021)
- [Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain](https://mlsanity.com/paper/1903.09245/) (2019)
- [Parameterizing the cost function of Dynamic Time Warping with application to time series classification](https://mlsanity.com/paper/2301.10350/) (2023)
- [Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment and Averaging](https://mlsanity.com/paper/2502.06591/) (2025)
- [Video alignment using unsupervised learning of local and global features](https://mlsanity.com/paper/2304.06841/) (2023)
- [Deep Attentive Time Warping](https://mlsanity.com/paper/2309.06720/) (2023)
- [Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data](https://mlsanity.com/paper/1711.01589/) (2017)

## Abstract (arXiv)

Time Series Alignment is a critical task in signal processing with numerous real-world applications. In practice, signals often exhibit temporal shifts and scaling, making classification on raw data prone to errors. This paper introduces a novel approach for Multiple Time Series Alignment (MTSA) leveraging Deep Learning techniques. While most existing methods primarily address Multiple Sequence Alignment (MSA) for protein and DNA sequences, there remains a significant gap in alignment methodologies for numerical time series. Additionally, conventional approaches typically focus on pairwise alignment, whereas our proposed method aligns all signals in a multiple manner (all the signals are aligned together at once). This innovation not only enhances alignment efficiency but also significantly improves computational speed. By decomposing into piece-wise linear sections, we introduce varying levels of complexity into the warping function. Additionally, our method ensures the satisfaction of three warping constraints: boundary, monotonicity, and continuity conditions. The utilization of a deep convolutional network allows us to employ a new loss function, addressing some limitations of Dynamic Time Warping (DTW). Experimental results on the UCR Archive 2018, comprising 129 time series datasets, demonstrate that employing our approach to align signals significantly enhances classification accuracy and warping average and also reduces the run time across the majority of these datasets.