Last month, I successfully defended my Master’s thesis titled “MultiETSC: Automated Machine Learning for Time Series Classification”. I have been working on my MultiETSC project for a little over a year as a part of the ADA group, supervised by Can Wang, Mitra Baratchi and Holger H. Hoos. In this project, we developed an AutoML approach to the problem of early time series classification (ETSC).
ETSC is a problem that is relevant for time-critical applications of time series classifications. It arises from the desire to get classification output before the full time series is observed. ETSC requires a classifier to decide at what point to classify based on the partially observed time series. This decision trades off the earliness of the classification for the accuracy of the classification. Therefore, any ETSC algorithm is faced with two competing objectives: earliness and accuracy.
For MutliETSC, we took eight existing and one naive self-produced ETSC algorithms and partly rewrote them to share a common interface. On this set of algorithms, we applied the MO-ParamILS configurator, which can optimise multiple objectives simultaneously. MultiETSC produces a set of configurations that give multiple options for trading off earliness against accuracy, while non of these configurations is strictly better than the other. This allows a user to pick a configuration that fits the ETSC problem at hand.
In our research, we show that leaving out either the algorithm selection or the multi-objective optimisation parts will both result in inferior results. This means that both these aspects significantly contribute to the quality of MultiETSC’s output.
If you want to know more about this project, have a look at the project page.
Finishing this project, sadly means I will be leaving the ADA group, but I am happy to have had the opportunity to work in such a great environment with such smart and motivated people as in the ADA group.