Local Gaussian Process Model Inference Classification for Time Series Data

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.

• Publication year

  2021

• Venue

  International Conference on Statistical and Scientific Database Management

• Publication date

  2021-07-06

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/3468791.3468839
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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