  Multitask learning (MTL) aims to develop a unified model that can handle a
set of closely related tasks simultaneously. By optimizing the model across
multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of
generalizability. Although MTL has been extensively researched in various
domains such as computer vision, natural language processing, and
recommendation systems, its application to time series data has received
limited attention. In this paper, we investigate the application of MTL to the
time series classification (TSC) problem. However, when we integrate the
state-of-the-art 1D convolution-based TSC model with MTL, the performance of
the TSC model actually deteriorates. By comparing the 1D convolution-based
models with the Dynamic Time Warping (DTW) distance function, it appears that
the underwhelming results stem from the limited expressive power of the 1D
convolutional layers. To overcome this challenge, we propose a novel design for
a 2D convolution-based model that enhances the model's expressiveness.
Leveraging this advantage, our proposed method outperforms competing approaches
on both the UCR Archive and an industrial transaction TSC dataset.
