Skip to main content




Multivariate time series classification in time-sensitive environments using deep learning

From: 2024-02-07 10:15 to 11:15 Seminarium

Hannes Östergrens presents his Master's thesis Wednesday 7 February at 10:15-11:15 in MH:333

In this thesis, sensor data from substations and distribution centrals in Sweden and Germany is analysed and used to predict the voltage environment which the device is within. The theory needed for understanding the measured values is explained, and the notion of multivariate time series is introduced along with the reason for using deep learning to solve the problem at hand. Several ways of extracting features to visualize data and improve classification accuracy are introduced, and the resulting plots are analyzed to give insight into what results can be expected from the training phase. Furthermore, common model architectures and state-of-the-art models are explained, as well as why they are suitable as comparators in the thesis. An extensive comparison between algorithms and deep learning models is then carried out to find the suitability of different models for the classification task by comparing accuracy, inference time (forward propagation time) and storage space (model size). Finally, the results and their implications are discussed along with the assumptions made while collecting the data to give the reader an understanding of the results. Improvements on the method used, issues that were encountered during the thesis and ideas for future work are also discussed.

Om händelsen
From: 2024-02-07 10:15 to 11:15


karl [dot] astrom [at] math [dot] lth [dot] se