Neural Time Warping For Multiple Sequence Alignment

TL;DR

This paper introduces neural time warping (NTW), a neural network-based approach that relaxes the multiple sequence alignment problem into a continuous optimization, enabling efficient alignment of many time-series and producing meaningful average sequences.

Contribution

The paper presents NTW, a novel neural network method that relaxes discrete MSA into continuous optimization, improving efficiency and scalability over traditional dynamic programming approaches.

Findings

Successfully aligns over a hundred time-series.

Outperforms existing MSA methods in accuracy.

Generates meaningful average time-series data.

Abstract

Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically solved by dynamic programming. However, the computational complexity increases exponentially with respect to the number of input sequences. In this paper, we propose neural time warping (NTW) that relaxes the original MSA to a continuous optimization and obtains the alignments using a neural network. The solution obtained by NTW is guaranteed to be a feasible solution for the original discrete optimization problem under mild conditions. Our experimental results show that NTW successfully aligns a hundred time-series and significantly outperforms existing methods for solving the MSA problem. In addition, we show a method for obtaining average time-series data as one of applications of NTW. Compared to the existing barycenters, the mean time series data retains the features of the input time-series data.