TY  - EDBOOK
AB  - 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. 
In 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.
DA  - 2016
KW  - structured data
KW  - gaussian processes
KW  - time series prediction
LA  - eng
PY  - 2016
SN  - 978-2-87587-026-1
TI  - Gaussian process prediction for time series of structured data
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29006762
Y2  - 2024-11-23T12:06:43
ER  -