Andre Gensler
Orcid: 0000-0002-5462-712X
According to our database1,
Andre Gensler
authored at least 14 papers
between 2013 and 2020.
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Bibliography
2020
CoRR, 2020
2018
Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria.
Pattern Anal. Appl., 2018
Proceedings of the 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2018
2017
Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
2016
A review of deterministic error scores and normalization techniques for power forecasting algorithms.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016
Deep Learning for solar power forecasting - An approach using AutoEncoder and LSTM Neural Networks.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016
Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
2015
Fast Feature Extraction for Time Series Analysis Using Least-Squares Approximations with Orthogonal Basis Functions.
Proceedings of the 22nd International Symposium on Temporal Representation and Reasoning, 2015
2014
Proceedings of the 16th LWA Workshops: KDML, 2014
Proceedings of the 22nd International Conference on Pattern Recognition, 2014
Location based learning of user behavior for proactive recommender systems in car comfort functions.
Proceedings of the 44. Jahrestagung der Gesellschaft für Informatik, Big Data, 2014
Temporal data analytics based on eigenmotif and shape space representations of time series.
Proceedings of the IEEE China Summit & International Conference on Signal and Information Processing, 2014
2013
Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations.
Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013