Instance Explainable Temporal Network For Multivariate Timeseries

Although deep networks have been widely adopted, one of their shortcomings has been their blackbox nature. One particularly difficult problem in machine learning is multivariate time series (MVTS) classification. MVTS data arise in many applications and are becoming ever more pervasive due to explosive growth of sensors and IoT devices. Here, we propose a novel network (IETNet) that identifies the important channels in the classification decision for each instance of inference. This feature also enables identification and removal of non-predictive variables which would otherwise lead to overfit and/or inaccurate model. IETNet is an end-to-end network that combines temporal feature extraction, variable selection, and joint variable interaction into a single learning framework. IETNet utilizes an 1D convolutions for temporal features, a novel channel gate layer for variable-class assignment using an attention layer to perform cross channel reasoning and perform classification objective. To gain insight into the learned temporal features and channels, we extract region of interest attention map along both time and channels. The viability of this network is demonstrated through a multivariate time series data from N body simulations and spacecraft sensor data.

Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features

Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objec tive. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, we apply a visualization method that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, we show that the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.