×

zbMATH — the first resource for mathematics

Is time fuzzy? (English) Zbl 1451.62167
Kreinovich, Vladik (ed.), Statistical and fuzzy approaches to data processing, with applications to econometrics and other areas. In honor of Hung T. Nguyen’s 75th birthday. Cham: Springer. Stud. Comput. Intell. 892, 47-54 (2021).
Summary: Imprecision of time measurements, subjective perception of time, flexible management of time, are examples of reasons to use a fuzzy modeling of time. Although all fuzzy set-based knowledge representations can be applied to time, its particular nature leads to specific treatments. We give examples of fuzzy methods to deal with time, in temporal reasoning, linguistic summarization of data, forecasting and scoring and also in spatio-temporal reasoning.
For the entire collection see [Zbl 1448.62015].
MSC:
62R07 Statistical aspects of big data and data science
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62M86 Inference from stochastic processes and fuzziness
03E72 Theory of fuzzy sets, etc.
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] https://physics.nist.gov/cuu/Units/second.html
[2] http://www.hawking.org.uk/the-beginning-of-time.html
[3] C. Cayol, Pourquoi les Chinois ont-ils le temps? Tallandier (2017)
[4] D. Dubois, H. Prade, Processing fuzzy temporal knowledge. IEEE Trans. Syst. Man Cybern. 19(4), 729-743 (1989)
[5] S. Badaloni, M. Giacomin, The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning. Artif. Intell. 170(10), 872-908 (2006) · Zbl 1131.68530
[6] J. Allen, Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832-843 (1983) · Zbl 0519.68079
[7] S. Schockaert, M. De Cock, Temporal reasoning about fuzzy intervals. Artif. Intell. 172(8-9), 1158-1193 (2008) · Zbl 1183.68620
[8] L. Vila, L. Godo, On fuzzy temporal constraint networks. Mathware Soft Comput. 1-3, 315-334 (1994) · Zbl 0833.68012
[9] D. Dubois, H. Fargier, H. Prade, Fuzzy constraints in job-shop scheduling. J Intell. Manuf. 6, 215-235 (1995)
[10] M.A. Cardenas Viedma, R. Main Morales, I. Navarrete Sanchez, Fuzzy temporal constraint logic: a valid resolution principle. Fuzzy Sets Syst. 117(2), 231-250 (2001) · Zbl 0984.03021
[11] D. Dubois, H. Fargier, P. Fortemps, Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge. Eur. J. Oper. Res. 147(2), 231-252 (2003) · Zbl 1037.90028
[12] S. Barro, R. Marín, J. Mira, A. Patón, A model and a language for the fuzzy representation and handling of time. Fuzzy Sets Syst. 61(2), 153-175 (1994)
[13] R. Marín, S. Barro, F. Palacios, R. Ruiz, F. Martin, An approach to fuzzy temporal reasoning in medicine. Mathware Soft Comput. 3, 265-276 (1994)
[14] P. Cariñena, A. Bugarín, M. Mucientes, S. Barro, A language for expressing fuzzy temporal rules. Mathware Soft Comput. VII(2-3), 22 (2000) · Zbl 0995.68058
[15] R. Yager, A new approach to the summarization of data. Inf. Sci. 28(1), 69-86 (1982) · Zbl 0517.94027
[16] J. Kacprzyk, R. Yager, “Softer” optimization and control models via fuzzy linguistic quantifiers. Inf. Sci. 34(2), 157-178 (1984) · Zbl 0562.90098
[17] M.-J. Lesot, G. Moyse, B. Bouchon-Meunier, Interpretability of fuzzy linguistic summaries. Fuzzy Sets Syst. 292, 307-317 (2016) · Zbl 1381.03025
[18] J. Casillas, O. Cordón, F. Herrera, L. Magdalena, in Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview, ed. by J. Casillas, O. Cordón, F. Herrera, L. Magdalena. Interpretability Issues in Fuzzy Modeling. Studies in Fuzziness and Soft Computing, vol. 128 (Springer, Berlin, 2003), pp. 3-22 · Zbl 1048.93003
[19] R. Castillo-Ortega, N. Marín, D. Sánchez, A. Tettamanzi, in Linguistic Summarization of Time Series Data using Genetic Algorithms, EUSFLAT 2011.145 (2011)
[20] J. Kacprzyk, A. Wilbik, S. Zadrozny, Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets Syst. 159(12), 1485-1499 (2008) · Zbl 1179.68121
[21] J. Kacprzyk, A. Wilbik, S. Zadrożny, Linguistic summarization of trends: a fuzzy logic based approach, in Proceedings of the 11th International Conference Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 2006, Paris, France, 2006), pp. 2166-2172 · Zbl 1106.68104
[22] J. Kacprzyk, A. Wilbik, S. Zadrożny, Linguistic summaries of time series via a quantifier based aggregation using the Sugeno integral, in Proceedings of 2006 IEEE World Congress on Computational Intelligence (WCCI 2006, Vancouver, BC, Canada, 2006), pp. 3610-3616 · Zbl 1122.68142
[23] J. Kacprzyk, A. Wilbik, S. Zadrożny, Linguistic summarization of time series by using the Choquet integral, in IFSA 2007, vol. 4529, LNCS (LNAI), ed. by P. Melin, O. Castillo, L.T. Aguilar, J. Kacprzyk, W. Pedrycz (Springer, Heidelberg, 2007), pp. 284-294 · Zbl 1122.68142
[24] J. Kacprzyk, A. Wilbik, S. Zadrożny, Linguistic summaries of time series via an OWA operator based aggregation of partial trends, in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007, London, UK; IEEE Press, Los Alamitos, 2007), pp. 467-472 · Zbl 1122.68142
[25] R. Castillo-Ortega, N. Mann, D. Sánchez, Linguistic local change comparison of time series, in 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), (Taipei, 2011), pp. 2909-2915
[26] R. Almeida, M.-J. Lesot, B. Bouchon-Meunier, U. Kaymak, G. Moyse, Linguistic summaries of categorical time series for septic shock patient data, in Fuzz-IEEE 2013—IEEE International Conference on Fuzzy Systems, Hyderabad, India (2013), pp. 1-8
[27] G. Moyse, M.-J. Lesot, B. Bouchon-Meunier, Linguistic summaries for periodicity detection based on mathematical morphology, in Proceedings of IEEE SSCI FOCI’13 (2013), pp. 106-113
[28] G. Moyse, M.-J. Lesot, Linguistic summaries of locally periodic time series. Fuzzy Sets Syst. 285, 94-117 (2016)
[29] G. Moyse, M.J. Lesot, Fast and incremental erosion score computation, in IPMU 2014 - International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Communications in Computer and Information Science, Montpellier, vol. 442, France (Springer, Berlin, 2014), pp. 376-385 · Zbl 1419.68201
[30] Q. Song, B. Chissom, Fuzzy time series and its model. Fuzzy Sets Syst. 54, 269-277 (1993) · Zbl 0792.62087
[31] E. Bulut, Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach. Expert Syst. Appl. 41(4), 1806-1812 (2014)
[32] V. Novák, M. Štěpnička, A. Dvořák, I. Perfilieva, V. Pavliska, L. Vavříčková, Analysis of seasonal time series using fuzzy approach. Int. J. Gen Syst. 39(3), 305-328 (2010) · Zbl 1191.62162
[33] T. Delavallade, L. Mouillet, B. Bouchon-Meunier, E. Collain, Monitoring event flows and modelling scenarios for crisis prediction, application to ethnic conflicts forecasting. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 15(S1), 83-110 (2007)
[34] M.-J. Lesot, T. Delavallade, F. Pichon, H. Akdag, B. Bouchon-Meunier, P. Capet, Proposition of a semi-automatic possibility information scoring process, in Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011, Aix-les-Bains, France (Atlantis Press, 2011), pp. 949-956
[35] T. Xiang, M.K.H. Leung, S.Y. Cho, Expression recognition using fuzzy spatio-temporal modeling. Pattern Recogn. 41(1), 204-216 (2008) · Zbl 1122.68570
[36] A. Sözer, A. Yazıcı, H. Oğuztüzün, O. Taş, Modeling and querying fuzzy spatiotemporal databases. Inf. Sci. 178(19), 3665-3682 (2008)
[37] J.M. Le Yaouanc, J.P. Poli, in A Fuzzy Spatio-temporal-Based Approach for Activity Recognition, ed. by S. Castano, P. Vassiliadis, L.V. Lakshmanan, M.L. Lee. Advances in Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol. 7518 (Springer, Berlin, 2012)
[38] V. Eude, Modélisation spatio-temporelle floue pour la reconnaissance d’activités militaires, Doctoral dissertation. Université Paris 6, 1998 (1998)
[39] M.-C. Su, A fuzzy rule-based approach to spatio-temporal hand gesture recognition. IEEE Trans. Syst. Man, Cybern. Part C (Appl. Rev.) 30(2), 276-281 (2000)
[40] N. Díaz Rodríguez, M. Cuéllar. J. Lilius, M. Delgado Calvo-Flores, A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowl. Based Syst. (2014)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.