Denborazko serieen sailkapen goiztiarra helburu anitzeko optimizazio problema gisa aztertua

Zusammenfassung

One of the most prominent problems in the area of time series data mining is called supervised time series clasication. The goal of this problem is to build a model that predicts the classes of new unclassied series as accurately as possible, departing from a database of time series for which the class is known. As an extension of this problem, on some occasions, the data is collected over time, and, in order to avoid costs that incur in collecting new data or negative consequences that may arise when making late predictions, the goal is to make the class predictions as early as possible. In this context, the problem denominated early classication of time series arises, whose objective is to build a classier that is as accurate as possible, but at the same time, makes the class prediction as early as possible. It is logical to think that the more data points are made available, the more information we have about the time series and, so, it is easier to make accurate class predictions. On the contrary, if we want to make early class predictions, we will have less information and it will be more dicult to make accurate class predictions. Therefore, accuracy and earliness are two objectives which are in con ict. In this work, we propose a innovate method for early classication based on multi-objective formulation of the problem. We have compared it to a model proposed in the literature which models the problem as a single objective optimization problem and we have seen that our model provides better results on some benchmark datasets.