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.
Nourbakhsh, A. and Mohammadzade, H. (2025). Deep Time Warping for Multiple Time Series Alignment. Scientia Iranica, (), -. doi: 10.24200/sci.2025.66136.9879
MLA
Nourbakhsh, A. , and Mohammadzade, H. . "Deep Time Warping for Multiple Time Series Alignment", Scientia Iranica, , , 2025, -. doi: 10.24200/sci.2025.66136.9879
HARVARD
Nourbakhsh, A., Mohammadzade, H. (2025). 'Deep Time Warping for Multiple Time Series Alignment', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2025.66136.9879
CHICAGO
A. Nourbakhsh and H. Mohammadzade, "Deep Time Warping for Multiple Time Series Alignment," Scientia Iranica, (2025): -, doi: 10.24200/sci.2025.66136.9879
VANCOUVER
Nourbakhsh, A., Mohammadzade, H. Deep Time Warping for Multiple Time Series Alignment. Scientia Iranica, 2025; (): -. doi: 10.24200/sci.2025.66136.9879
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