Autor: Szupiluk, R. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Multiway Similarity Approach Based on Divergence Functions and Smoothness Measure
Autorzy:: Szupiluk, R.
Sobon, T.
Powiązania:: https://bibliotekanauki.pl/articles/1398839.pdf
Data publikacji:: 2016-05
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: In this paper we present a novel similarity measure method for financial data. In our approach, we propose the assessment of the similarity in a coherent hierarchical and multi-faceted way, following the general scheme where various detailed basic measures may be used like the Fermi-Dirac divergence, Bose-Einstein divergence, or our new smoothness measure. The presented method is tested on benchmark and real stock markets data.
Źródło:: Acta Physica Polonica A; 2016, 129, 5; 927-931
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Analysis of Financial Time Series Morphology with AMUSE Algorithm and Its Extensions
Autorzy:: Szupiluk, R.
Ząbkowski, T.
Soboń, T.
Powiązania:: https://bibliotekanauki.pl/articles/1398891.pdf
Data publikacji:: 2016-05
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: The proposed article presents a new approach to analyze the relationships between financial instruments. We use blind signal separation methods to decompose time series into the core components. The components common to the various instruments provide broad set of characteristics to describe the internal morphology of the time series. In this research a modified and extended version of AMUSE algorithm is used. The concept is presented based on real financial instruments.
Źródło:: Acta Physica Polonica A; 2016, 129, 5; 1018-1022
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 3.

Tytuł:: Independent Component Analysis for Ensemble Predictors with Small Number of Models
Autorzy:: Szupiluk, R.
Ząbkowski, T.
Gajowniczek, K.
Powiązania:: https://bibliotekanauki.pl/articles/1388270.pdf
Data publikacji:: 2015-03
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: The article presents independent component analysis (ICA) applied to the concept of ensemble predictors. The use of ICA decomposition enables to extract components with particular statistical properties that can be interpreted as destructive or constructive for the prediction. Such process can be treated as noise filtration from multivariate observation data, in which observed data consist prediction results. As a consequence of the ICA multivariate approach, the final results are combination of the primary models, what can be interpreted as aggregation step. The key issue of the presented method is the identification of noise components. For this purpose, a new method for evaluating the randomness of the signals was developed. The experimental results show that presented approach is effective for ensemble prediction taking into account different prediction criteria and even small set of models.
Źródło:: Acta Physica Polonica A; 2015, 127, 3A; A-139-A-144
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 4.

Tytuł:: Ensemble neural network approach for accurate load forecasting in a power system
Autorzy:: Siwek, K.
Osowski, S.
Szupiluk, R.
Powiązania:: https://bibliotekanauki.pl/articles/907659.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sieć neuronowa
ślepa separacja sygnałów
prognozowanie obciążenia
neural network
blind source separation
ensemble of predictors
load forecasting
Opis:: The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 303-315
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Szupiluk, R." wg kryterium: Autor

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język