LOGIC: Probabilistic Machine Learning for Time Series Classification

Abstract

Time series data is one of the complex data types commonly encountered in many application areas ranging from automotive, finance, medicine to industry. A prominent task is time series classification, which entails identifying expressive features in oder to predict 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 concept consists in (i) learning latent characteristics of given time series data by means of Gaussian processes, (ii) using these characteristics to embed time series into a more expressive feature space and (iii) classifying time series data based on these features via existing classification methods. By making use of various general-purpose classification methods, we show that LOGIC is able to compete with state-of-the-art approaches in terms of accuracy and efficiency.