Predicting the Probability of Exceeding Critical System Thresholds
In this paper we show how regression modelling can be combined with a special kind of data transformation technique that improves model precision and produces several “preliminary” estimates of the target value. These preliminary estimates can be used for interval estimates of the target value as we...
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| Дата: | 2018 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
PROBLEMS IN PROGRAMMING
2018
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| Теми: | |
| Онлайн доступ: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/282 |
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| Назва журналу: | Problems in programming |
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Репозитарії
Problems in programming| Резюме: | In this paper we show how regression modelling can be combined with a special kind of data transformation technique that improves model precision and produces several “preliminary” estimates of the target value. These preliminary estimates can be used for interval estimates of the target value as well as for predicting the probability that it has or will exceed arbitrary predefined thresholds. Our approach can be combined with various regression models and applied in many domains that need to estimate the probability of system malfunctions or other hazardous states brought about by system variables exceeding critical safety thresholds. We rigorously derive the formulas for the probability of crossing an upper bound and a lower bound both separately (one-sided intervals) and together (a two-sided interval), and verify the approach experimentally on a real dataset from the electric power industry.Problems in programming 2018; 2-3: 189-196 |
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