Decision Graph Forecasting Event Stream Temporal Dependencies
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Solution Overview
Problem
Existing methods for modeling temporal dependencies in event streams, such as Dynamic Bayesian Networks, face challenges in handling continuous-time data and large numbers of event types, leading to poor representation, increased costs, and intractability due to discretization issues and long-term dependency conditioning.
Innovation Solution
A method involving the generation of decision graphs from event stream data using Conditional Intensity Models, specifically the Conjoint Piecewise-Constant Conditional Intensity Model (C-PCIM), which allows for shared structure and parameters among event types, enabling efficient modeling of temporal dependencies and rare event handling through a decision tree-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Dynamic Bayesian Networks are used to model temporal dependencies in event streams, then the model can capture temporal relationships between events, but the model becomes intractable when handling large numbers of event types and long-term dependencies
Solution Approach 1:
The patent segments the event stream modeling into multiple time intervals or windows, where each window captures local temporal dependencies. Instead of modeling all events globally, the system divides the continuous event stream into manageable segments, reducing the complexity of long-term dependency conditioning while preserving local temporal relationships.
Solution Approach 2:
The patent extracts and focuses on the most relevant features and event types for temporal dependency modeling, rather than attempting to model all event types equally. By identifying and extracting key patterns and dependencies, the system reduces the effective model complexity while maintaining reliability for the most important temporal relationships.
2Measurement precision
If the sampling rate is increased to better represent continuous-time data, then the data representation improves, but the number of samples increases making learning and inference more costly
Solution Approach 1:
The patent applies partial sampling strategies where not all time points are sampled at the same rate. Instead of uniformly high sampling across all intervals, the system uses adaptive sampling that concentrates measurements in critical regions or time intervals where events are more frequent or important, achieving good representation with fewer total samples.
Solution Approach 2:
The patent changes the sampling rate parameter dynamically based on event density and temporal patterns. Rather than using a fixed high sampling rate, the system adjusts the sampling interval adaptively, using faster sampling when events are frequent and slower sampling when events are sparse, thereby maintaining data representation quality while reducing overall computational cost.
3Productivity
If the sampling rate is decreased to reduce computational cost, then learning and inference become more efficient, but the data representation becomes poor
Solution Approach 1:
The system applies partial sampling focused on the most informative time intervals and event types. By concentrating computational resources on critical segments of the event stream rather than uniformly sampling all data, the system maintains good representation quality for the most important patterns while reducing overall computational burden.
4Reliability
If the model conditions on multiple past steps to capture long-term dependencies, then the temporal modeling accuracy improves, but the number of conditioning variables increases making the model intractable
Solution Approach 1:
The patent extracts and retains only the most relevant historical events and features for conditioning, rather than conditioning on all past events. By identifying and extracting key dependency patterns and summarizing historical information, the system captures long-term dependencies with a manageable number of conditioning variables.
Solution Approach 2:
The patent changes the effective conditioning window or horizon parameter adaptively. Instead of always conditioning on a fixed large number of past steps, the system adjusts the conditioning depth based on event types and temporal patterns, using longer conditioning horizons when necessary for capturing long-term dependencies and shorter horizons when recent events are sufficient.
Data Source
AI summary
Methods and systems for forecasting a future event in an event stream are provided herein. The event stream includes a number of events, wherein each of the events includes an event label and an associated time stamp. The system includes a processor that is adapted to execute stored instructions and a system memory. The system memory includes code configured to generate one or more decision graphs for predicting events with event labels chosen from a number of event labels based on the events within the event stream, wherein the decision graph includes decision nodes and leaf nodes, and wherein each of the decision nodes includes a question. The system memory also includes code configured to analyze event stream data using the decision graph and forecast a future event in the event stream based on the analysis of the event stream data.