Count Predictive Model with Mixed Categorical and Count Explanatory Variables

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00575361" target="_blank" >RIV/67985556:_____/23:00575361 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/23:00368072
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Count Predictive Model with Mixed Categorical and Count Explanatory Variables
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/8A21009" target="_blank" >8A21009: Embedded storage elements on next MCU generation ready for AI on the edge</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS'2023
ISBN
979-8-3503-5804-9
ISSN
2770-4254
e-ISSN
2770-4254
Number of pages
6
Pages from-to
51-56
Publisher name
IEEE
Place of publication
Piscataway
Event location
Dortmund
Event date
Sep 7, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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