Sharif University of Technology Scientia Iranica 1026-3098 Articles in Press 2025 08 05 Deep Time Warping for Multiple Time Series Alignment 23939 10.24200/sci.2025.66136.9879 EN Alireza Nourbakhsh Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran Hoda Mohammadzade Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran 0000-0002-9852-5088 Journal Article 2025 02 08 Time Series Alignment is a crucial task in signal processing with wide-ranging applications. Real-world signals often suffer from temporal shifts and scaling, leading to errors in raw data classification. This paper presents a novel Deep Learning-based approach for Multiple Time Series Alignment (MTSA). While existing methods mainly focus on Multiple Sequence Alignment (MSA) for biological sequences, there is a notable lack of alignment techniques for numerical time series. Traditional methods also typically address pairwise alignment, whereas our approach aligns all signals simultaneously, improving both alignment efficiency and computational speed. By decomposing to piece-wise linear sections, we introduce varying complexity into the warping function while ensuring compliance with three key constraints: boundary, monotonicity, and continuity conditions. We propose a deep convolutional network with a novel loss function that addresses key limitations of Dynamic Time Warping (DTW). Experiments on the UCR Archive 2018, involving 129 time series datasets, show that our method significantly enhances classification accuracy, warping average, and runtime efficiency across most datasets. https://scientiairanica.sharif.edu/article_23939_c707fcaa3ec072bc292233ce2983ddae.pdf