Modeling of monitoring processes with uneven and fuzzy observation intervals
DOI:
https://doi.org/10.34185/1562-9945-4-135-2021-14Keywords:
моніторинг, нерівномірна у часі вибірка, сепарабельна модель, нечітка квантильна модель, моніторинг стану хворихAbstract
The paper presents the results of applying a separable mathematical model for analyzing fuzzy time series with uneven and fuzzy data sampling intervals. The study of the efficiency of an advanced quantile modeling algorithm is presented. The implementation of models of measurement sequences with fuzzy steps is conducting by applying the approach based on α-levels. The center of weight method was used for scalarization the fuzzy result. A separable model was used for modeling the processes of clinical monitoring of patients with diabetes.
References
Q. Song, and B. S. Chissom, “Forecasting enrollments with fuzzy time series — Part I,” Fuzzy Sets and Systems, vol. 54, issue 1, 1993a, pp. 1¬9.
B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets and Systems, vol. 54, issue 3, 1993b, pp. 269-277.
S. M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets and Systems, vol. 81, 1996, pp. 311-319.
Pegat A. “Fuzzy modeling”, – М. BINOM, 2009, 798p.
R. Koenker, “Quantile Regression”, Cambridge University Press,NY- 2005.pp. 137–143.
Rutkovskiy L. Methods and technology of artificial intellect. – М. Hot line – Telecom, 210. – 520 с.
Tahseen A., Aqil S., Burney Cemal A. A New Quantile Based Fuzzy Time Series Forecasting Model [Електронний ресурс] – Режим доступу:
https://publications.waset.org/14214/pdf
E. Bas, U. Yolcu and E. Egrioglu, “Intuitionistic fuzzy time series functions approach for time series forecasting”, Granular Computing, 2020.
S.S. Pal and S. Kar, “Fuzzy Time Series Model for Unequal Interval Length Using Genet-ic Algorithm” Advances in Intelligent Systems and Computing, vol. 699 2019.
J.L. Bernal, S. Cummins, A. Gasparrini “Interrupted time series regression for the evaluation of public health interventions: a tutorial”, International Journal of Epidemiol-ogy, vol. 46, Issue 1, 2016 рр. 348–355.
R. Gao, O. Duru “Parsimonious fuzzy time series modeling”, Expert Systems With Applications vol. 156, 2020.
Carla S. M¨oller-Levet, F. Klawonn, Kwang-Hyun Cho and O. Wolkenhauer, “Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points”, Advances in Intelligent Data Analysis V, 2003 pp. 330–340.
W. Koo, Shin Wee Wong, G. Selvachandran, Hoang Viet Long, Le Hoang Son, “Predic-tion of Air Pollution Index in Kuala Lumpur using fuzzy time series and statistical mod-els”, Air Quality, Atmosphere & Health vol. 13, 2019 pp. 77-88
T. A. Jilani, S. M. A. Burney, and C. Ardil, “Multivariate high order fuzzy time series forecasting for car road accidents,” International Journal of Computational Intelligence, vol. 4, issue 1,2007b, pp. 15-20.
Models and methods of socio-economic forecasting / Geyets V.M., Klebanova T.S., Chernyak O.I. – Kharkiv: PH « INZHEK», 205. – 396 p.
Methods of intelligent modeling of processes with a variable observation interval and constructive ordering “with weight” / V.V. Skalozub, B.B. Belyy, O.O. Galabut, О.V. Murashov // System technologies, 2020, vol. 5 (132). – Р. 83-98.
