Modeling Multivariate Time Series via Prototype Learning: A Multi-Level Attention-based Perspective

Recently, the modeling and representation of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its wide application potentials in biomedicine, finance, industry and so on. During the last decade, deep learning has achieved great success in many tasks. However, a large number of labeled data samples are needed to train a satisfactory model which has a huge amount of parameters, especially in cases that the inputs are multivariate time series (i.e., multi-dimension) and have complex relationships with the outputs. We propose a Multi-level attention-based prototype Network (MapNet) to model multivariate time series. Specifically, we first encode the time series based on deep learning and calculate the prototype for each class. Afterwards, we propose a multi-level attention mechanism to further optimize the prototype, including a short-term encoder as well as a long-term encoder. Experiments based on two public datasets demonstrate that MapNet outperforms state-of-the-art baseline models and is more applicable for few-shot dataset.