  Learning representation from unlabeled time series data is a challenging
problem. Most existing self-supervised and unsupervised approaches in the
time-series domain do not capture low and high-frequency features at the same
time. Further, some of these methods employ large scale models like
transformers or rely on computationally expensive techniques such as
contrastive learning. To tackle these problems, we propose a non-contrastive
self-supervised learning approach efficiently captures low and high-frequency
time-varying features in a cost-effective manner. Our method takes raw time
series data as input and creates two different augmented views for two branches
of the model, by randomly sampling the augmentations from same family.
Following the terminology of BYOL, the two branches are called online and
target network which allows bootstrapping of the latent representation. In
contrast to BYOL, where a backbone encoder is followed by multilayer perceptron
(MLP) heads, the proposed model contains additional temporal convolutional
network (TCN) heads. As the augmented views are passed through large kernel
convolution blocks of the encoder, the subsequent combination of MLP and TCN
enables an effective representation of low as well as high-frequency
time-varying features due to the varying receptive fields. The two modules (MLP
and TCN) act in a complementary manner. We train an online network where each
module learns to predict the outcome of the respective module of target network
branch. To demonstrate the robustness of our model we performed extensive
experiments and ablation studies on five real-world time-series datasets. Our
method achieved state-of-art performance on all five real-world datasets.
