Prototypical Inception Network with Cross Branch Attention for Time Series Classification
Jingyu Sun, Susumu Takeuchi, Ikuo Yamasaki · 2021
Nowadays, the explosive growth of time series data and the idea of automatically classifying them brought chances of advanced analysis and machine cognitive processes in various domains. Hundreds of Time Series Classification (TSC) algorithms have been proposed during the last decade. Most of them need the large quantity of labeled training data for achieving good precision. However, we noticed that under most of the training scenarios, the large-scale supervised training datasets are not readily available. We thus proposed a few shot deep learning neural network framework PIN-BA (Prototypical Inception Network with Cross Branch Attention) for time series classification with only limited training data. We use CNN (Convolutional Neural Network) with branches of different reception windows for capturing the features of different time window scales. We design cross branch attention schemes based on prototypical networks to emphasize the crucial features' information during classification. A few experiments were conducted on the famous UCR Time Series Data Sets. Experimental results demonstrate that our proposed framework outperforms the other TSC models, such like 1NN-DTW and InceptionTime under the few shot training data scenarios.