| Classification of edible oils using synchronous scanning fluorescence spectroscopy E Sikorska, T Górecki, IV Khmelinskii, M Sikorski, J Kozioł Food Chemistry 89 (2), 217-225, 2005 | 235 | 2005 |
| Using derivatives in time series classification T Górecki, M Łuczak Data Mining and Knowledge Discovery 26, 310-331, 2013 | 172 | 2013 |
| Multivariate time series classification with parametric derivative dynamic time warping T Górecki, M Łuczak Expert Systems with Applications 42 (5), 2305-2312, 2015 | 151 | 2015 |
| A comparison of tests for the one-way ANOVA problem for functional data T Górecki, Ł Smaga Computational Statistics 30, 987-1010, 2015 | 102 | 2015 |
| Systemy uczące się M Krzyśko, W Wołyński, T Górecki, M Skorzybut Rozpoznawanie wzorców, analiza skupień i redukcja wymiarowości. WNT, Warszawa, 2008 | 102 | 2008 |
| Non-isometric transforms in time series classification using DTW T Górecki, M Łuczak Knowledge-based systems 61, 98-108, 2014 | 97 | 2014 |
| Monitoring beer during storage by fluorescence spectroscopy E Sikorska, T Górecki, IV Khmelinskii, M Sikorski, D De Keukeleire Food Chemistry 96 (4), 632-639, 2006 | 83 | 2006 |
| Fluorescence spectroscopy for characterization and differentiation of beers E Sikorska, T Górecki, IV Khmelinskii, M Sikorski, D De Keukeleire Journal of the Institute of Brewing 110 (4), 267-275, 2004 | 71 | 2004 |
| Selected statistical methods of data analysis for multivariate functional data T Górecki, M Krzyśko, Ł Waszak, W Wołyński Statistical Papers 59, 153-182, 2018 | 68 | 2018 |
| Konwergencja regionalna E Nowińska-Łaźniewska, T Górecki, R Chmielewski Wydawnictwo Uniwersytetu Ekonomicznego, 2011 | 68 | 2011 |
| fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data T Górecki, Ł Smaga Computational Statistics 34 (2), 571-597, 2019 | 60 | 2019 |
| Multivariate analysis of variance for functional data T Górecki, Ł Smaga Journal of Applied Statistics 44 (12), 2172-2189, 2017 | 47 | 2017 |
| Using derivatives in a longest common subsequence dissimilarity measure for time series classification T Górecki Pattern Recognition Letters 45, 99-105, 2014 | 44 | 2014 |
| Perception of apple juice: A comparison of physicochemical measurements, descriptive analysis and consumer responses K Włodarska, K Pawlak‐Lemańska, T Górecki, E Sikorska Journal of Food Quality 39 (4), 351-361, 2016 | 43 | 2016 |
| Change point detection in heteroscedastic time series T Górecki, L Horváth, P Kokoszka Econometrics and statistics 7, 63-88, 2018 | 41 | 2018 |
| Testing normality of functional time series T Górecki, S Hörmann, L Horváth, P Kokoszka Journal of time series analysis 39 (4), 471-487, 2018 | 40 | 2018 |
| Classification of commercial apple juices based on multivariate analysis of their chemical profiles K Włodarska, K Pawlak-Lemańska, T Górecki, E Sikorska International Journal of Food Properties 20 (8), 1773-1785, 2017 | 39 | 2017 |
| Classification of time series using combination of DTW and LCSS dissimilarity measures T Górecki Communications in Statistics-simulation and Computation 47 (1), 263-276, 2018 | 37 | 2018 |
| GEval: Tool for debugging NLP datasets and models F Gralinski, A Wróblewska, T Stanisławek, K Grabowski, T Górecki Proceedings of the 2019 ACL workshop BlackboxNLP: analyzing and interpreting …, 2019 | 31 | 2019 |
| First and second derivatives in time series classification using DTW T Górecki, M Łuczak Communications in Statistics-Simulation and Computation 43 (9), 2081-2092, 2014 | 31 | 2014 |
Ewa SikorskaUniwersytet Ekonomiczny w PoznaniuZweryfikowany adres z ue.poznan.pl
