Administracija predavanja

Predavač: Tomislav Kovačević
Institucija: Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu
Naslov predavanja (HR): Optimal Trend Labeling in Financial Time Series
Naslov predavanja (EN): Optimal Trend Labeling in Financial Time Series
Tip predavanja: Seminar za optimizaciju i primjene
Datum i time: 11.03.2026., 12:00
Predavaonica: Predavaonica 2
Sažetak (HR): Predicting asset price trends is often posed as a classification problem, where trends are classified as positive or negative. Since asset price series are noisy and volatile, it is difficult to distinguish true trends from short-term fluctuations. To this end, several trend definitions have been proposed in the literature, but it is yet to be known how these trend definitions affect the performance of classification algorithms designed to learn such labels from historical data. In this paper, we define the robustness of the trend labeling algorithm as a measure of how well a classifier designed to learn such labels can withstand a change in the cumulative return considering the classifier’s generalization error. Moreover, we propose a noise model to simulate the desired accuracy score, which allows us to evaluate the robustness of a trend labeling algorithm without the need to train an actual classifier and consequently choose the optimal algorithm in terms of robustness. Experimental results confirm the adequacy of the proposed noise model and show that classification algorithms perform better when trained with such optimal labels.

Sažetak (EN): Predicting asset price trends is often posed as a classification problem, where trends are classified as positive or negative. Since asset price series are noisy and volatile, it is difficult to distinguish true trends from short-term fluctuations. To this end, several trend definitions have been proposed in the literature, but it is yet to be known how these trend definitions affect the performance of classification algorithms designed to learn such labels from historical data. In this paper, we define the robustness of the trend labeling algorithm as a measure of how well a classifier designed to learn such labels can withstand a change in the cumulative return considering the classifier’s generalization error. Moreover, we propose a noise model to simulate the desired accuracy score, which allows us to evaluate the robustness of a trend labeling algorithm without the need to train an actual classifier and consequently choose the optimal algorithm in terms of robustness. Experimental results confirm the adequacy of the proposed noise model and show that classification algorithms perform better when trained with such optimal labels.

Predavač: Davide Palitta
Institucija: Alma Mater Studiorum, Università di Bologna, Italija
Tip predavanja: Seminar za optimizaciju i primjene
Datum i time: 27.05.2026., 12:00
Predavaonica: Predavaonica 2

Predavač: Prof. dr. sc. Slobodan Filipovski
Institucija: Department of Mathematics, University of Primorska, Koper
Naslov predavanja (HR): Naslov će bit naknadno objavljen
Tip predavanja: Matematički kolokvij
Datum i time: 11.06.2026., 14:00
Predavaonica: Predavaonica 3