Year: 2016
Type: conference-paper
Abstract: Time series prediction constitutes a classic topic in machine learning with wide-ranging applications, but mostly restricted to the domain of vectorial sequence entries. In recent years, time series of structured data (such as sequences, trees or graph structures) have become more and more important, for example in social network analysis or intelligent tutoring systems. \nIn this contribution, we propose an extension of time series models to strucured data based on Gaussian processes and structure kernels. We also provide speedup techniques for predictions in linear time, and we evaluate our approach on real data from the domain of intelligent tutoring systems.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
Awards: G1927681210, G2330005221
OpenAlex ID: W2524962478