0575361 20240402214407.420230911235959.9 Count Predictive Model with Mixed Categorical and Count Explanatory Variables 6 s. P cav_un_epca*0575360979-8-3503-5804-9Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS'2023Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS'202351-56PiscatawayIEEE2023 count data Poisson mixtures Poisson regression recursive Bayesian mixture estimation cav_un_auth*0383037 Uglickich Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0452994 Reznychenko T. CZ http://library.utia.cas.cz/separaty/2023/ZS/uglickich-0575361.pdf 8A21009 GA MŠk cav_un_auth*0432581 The paper considers the problem of online prediction of a count variable based on real-time explanatory data of mixed count and categorical nature. The presented solution is based on (i) recursive Bayesian estimation of a mixture model of Poisson-distributed explanatory counts, using the categorical explanatory variable as a measurable pointer of the mixture, (ii) construction of a mixture of local Poisson regressions on the clustered data, and (iii) use of the pre-estimated mixtures for online prediction of the target count using actual measured explanatory data. The latter is one of the main contributions of the proposed approach. In addition, the dynamic model of the categorical explanatory variable preserves the functionality of the algorithm in case of its measurement failure. The experiments with simulations and real data report lower prediction errors compared to theoretical counterparts. cav_un_auth*0454569 The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS'2023 20230907 20230909 Dortmund DE BB 10000 10100 10103 2024 3 PR RVO:67985556 https://hdl.handle.net/11104/0345384 S 2023 SCOPUS PUBMED WOS cav_un_epca*0575360 Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS'2023 IEEE 2023 Piscataway 51 56 979-8-3503-5804-9 2770-4254