Neural Time Warping For Multiple Sequence Alignment
This addresses a challenging bottleneck in time-series analysis by providing a more efficient solution for researchers and practitioners dealing with large datasets.
The paper tackles the computational complexity of multiple sequence alignment (MSA) for time-series data by proposing neural time warping (NTW), which relaxes it to a continuous optimization solved with a neural network, and shows it successfully aligns a hundred time-series while significantly outperforming existing methods.
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.