Time Series Prediction for Relational and Kernel Data

Paaßen B (2017)
Bielefeld University.

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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.
Stichworte
Structured Data; Graphs; Time Series Prediction; Gaussian Processes; Kernel Space
Erscheinungsjahr
2017
Page URI
https://pub.uni-bielefeld.de/record/2913104

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.
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OA Open Access
Zuletzt Hochgeladen
2019-09-25T06:50:05Z
MD5 Prüfsumme
1e6daaa20c5acd17284ac9893d234a89

Material in PUB:
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.
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.

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