HMCKRAutoEncoder: An Interpretable Deep Learning Framework for Time Series Analysis

Analysis of time series data has long been a problem of great interest in a wide range of fields, such as medical surveillance, gene expression analysis, and economic forecasting. Recently, there has been a renewed interest in time series analysis with deep learning, since deep learning models can achieve state-of-the-art results on various tasks. However, deep learning models such as DNNs have a huge parametric space, which makes DNNs be viewed as complex “black-box” models. We propose a novel framework, HMCKRAutoEncoder, which adopts a two-task learning method to construct a human-machine collaborative knowledge representation (HMCKR) on a hidden layer of an AutoEncoder, to address the “black-box” problem in deep learning based time series analysis. In our framework, the AutoEncoder model is cross-trained by two learning tasks, aiming to generate HMCKR on a hidden layer of the AutoEncoder. We propose a pipeline for HMCKR-based time series analysis for various tasks. Moreover, a human-in-the-loop (HIL) mechanism is introduced to provide humans with the ability to intervene with the decision-making of deep models. Experimental results on three datasets demonstrate that our method is consistently comparable with several state-of-the-art methods while providing interpretability, and outperforms these methods when the HIL mechanism is applied.