Local Gaussian Process Model Inference Classification for Time Series Data

Abstract

One of the prominent types of time series analytics is classification, which entails identifying expressive class-wise features for determining class labels of time series data. In this paper, we propose a novel approach for time series classification called Local Gaussian Process Model Inference Classification (LOGIC). Our idea consists in (i) approximating the latent, class-wise characteristics of given time series data by means of Gaussian processes and (ii) aggregating these characteristics into a feature representation to (iii) provide a model-agnostic interface for state-of-the-art feature classification mechanisms. By making use of a fully-connected neural network as classification model, we show that the LOGIC model is able to compete with state-of-the-art approaches.