Event Prediction System Using Distribution Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for predicting events rely on rudimentary modeling techniques that prioritize frequently occurring events, neglecting high-impact events which can disrupt the system and lead to improper resource allocation.
Innovation Solution
The system uses an event prediction application to analyze exposure types, determine suitable distribution models like extreme loss models, and implement automated countermeasures to mitigate high-impact events, ensuring proper resource allocation and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rudimentary modeling techniques are used to predict events, then the system can process frequently occurring events, but it fails to predict high-impact events and misallocates resources
Solution Approach 1:
The system changes the parameters of event prediction by switching from rudimentary modeling techniques to distribution models (such as Poisson distribution, Normal distribution, or Extreme Value Theory models) that can capture both frequent low-impact events and rare high-impact events. This parameter change enables accurate prediction of high-impact events while maintaining processing efficiency through automated resource allocation based on predicted event characteristics.
2Productivity
If the system focuses on frequently occurring events, then processing efficiency is maintained, but high-impact events are neglected causing system disruption
Solution Approach 1:
The system performs preliminary action by predicting high-impact events before they occur using distribution models. By estimating the probability and potential impact of rare events in advance, the system can prepare appropriate responses and allocate resources proactively, preventing system disruption while maintaining processing efficiency through automated early warning mechanisms.
3Reliability
If automated countermeasures are implemented for predicted events, then impact mitigation is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated countermeasures that are triggered automatically when high-impact events are predicted. The system autonomously executes pre-defined response actions, allocates resources, and adjusts operations without requiring manual intervention, thereby improving impact mitigation effectiveness while avoiding the complexity of manual control systems.
Data Source
AI summary
Embodiments of the present invention provide a system for predicting one or more events and mitigating the impact of the one or more events. The system is typically configured for presenting a list of exposures to a user via an event prediction application user interface, prompting the user to select an exposure from the list of exposures, receiving a selection of the exposure and at least one option from the user device, determining type of the exposure based on the at least one option, determining one or more distribution models based on the type of the exposure, estimating occurrence of the one or more events associated with the exposure using at least one distribution model from the one or more distribution models, and in response to estimating the occurrence of the one or more events, triggering one or more automated counter measures.