Time Series Prediction for Relational and Kernel Data

Paaßen B (2017)
Bielefeld University.

Download
OA 45.68 KB
Datenpublikation
Daten vorhanden für diesen Nachweis
Abstract / Bemerkung
This Matlab (R) toolbox provides algorithms to predict the future location of some object in a kernel / distance embedding space. This permits to apply time series prediction to non-vectorial data, such as sequences, trees and graphs. The input for this toolbox are time series of relational or kernel data given as distance or kernel matrices and successor mappings. The output are affine coefficients of training data points, which can be used to locate the predicted point relative to the training data or new data and apply other relational or kernel-based approaches on the predicted point. In more detail, this toolbox implements kernel regression (Nadaraya-Watson regression), Gaussian Processes and the robust Bayesian Committee machine and provides a demo script demonstrating the function of this toolbox.
Erscheinungsjahr
Data Re-Use License
This Time Series Prediction for Relational and Kernel Data is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
PUB-ID

Zitieren

Paaßen B. Time Series Prediction for Relational and Kernel Data. Bielefeld University; 2017.
Paaßen, B. (2017). Time Series Prediction for Relational and Kernel Data. Bielefeld University. doi:10.4119/unibi/2913104
Paaßen, B. (2017). Time Series Prediction for Relational and Kernel Data. Bielefeld University.
Paaßen, B., 2017. Time Series Prediction for Relational and Kernel Data, Bielefeld University.
B. Paaßen, Time Series Prediction for Relational and Kernel Data, Bielefeld University, 2017.
Paaßen, B.: Time Series Prediction for Relational and Kernel Data. Bielefeld University (2017).
Paaßen, Benjamin. Time Series Prediction for Relational and Kernel Data. Bielefeld University, 2017.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2017-10-05T08:29:55Z

Material in PUB:
Wissenschaftliche Version
Gaussian process prediction for time series of structured data
Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
In sonstiger Relation
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Paaßen B, Göpfert C, Hammer B (2018)
Neural Processing Letters 48(2): 669-689.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar