Open AccessComputational Probabilistic Analysis of Distributional Time Series
Author(s) -
Борис С. Добронец,
Olga A. Popova
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1680/1/012008
Subject(s) - probabilistic logic , time series , series (stratigraphy) , computer science , probabilistic analysis of algorithms , probability density function , probability distribution , symbolic data analysis , probabilistic relevance model , mathematics , statistics , artificial intelligence , machine learning , theoretical computer science , paleontology , biology
The article considers a new approach to forecasting of distributional time series based on computational probabilistic analysis. Functional data analysis and symbolic data analysis are currently used to study such data. A comparison of these approaches is given. Computational probabilistic analysis to forecasting of distributional time series uses special numerical operations on probability density functions. The article provides numerical examples of the analysis of distributional time series.