Temporal Classification Model for Resource Prefetching
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Solution Overview
Problem
Forecasting numerous independent events simultaneously with individual time-series models is costly in terms of computational resources, as it requires numerous models to predict events such as weather forecasts, network usage, or transportation service pickups, which becomes impractical for large numbers of events.
Innovation Solution
A trained temporal classification model predicts classification labels indicating whether events will occur or their frequency during future time intervals, transforming time-series datasets into temporal classification datasets based on features like seasonality, temporal representation, and statistical features, allowing for common classification analysis and actions like prefetching information or allocating resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual time-series models are used to forecast each event, then prediction accuracy is maintained, but computational resource consumption increases significantly
Solution Approach 1:
The patent combines multiple individual time-series models into a single unified temporal classification model that processes multiple events simultaneously. Instead of training and running separate models for each event (which would consume excessive computational resources), the system integrates them into one model that accepts multiple time-series datasets as input and produces classification labels for all events in parallel, thereby maintaining prediction accuracy while significantly reducing computational overhead
Solution Approach 2:
The unified temporal classification model serves multiple functions by handling diverse event types (e.g., weather forecasts, network usage predictions, transportation service pickups) through a single model architecture. This multi-functional approach allows the system to maintain specialized prediction capabilities for different event types while sharing common computational infrastructure, thus reducing overall resource consumption without sacrificing the specialized accuracy needed for each event type
2Measurement precision
If numerous individual models are deployed, then event-specific predictions are accurate, but system complexity increases
Solution Approach 1:
The patent merges the complexity of managing numerous individual models into a single unified model structure. By consolidating multiple event-specific prediction pathways into one temporal classification model, the system reduces the operational complexity of deploying, monitoring, and maintaining multiple separate models while preserving the ability to generate accurate event-specific predictions through the unified architecture
3Productivity
If a unified classification model is used, then resource usage is optimized, but the ability to capture event-specific patterns may be reduced
Solution Approach 1:
The unified temporal classification model incorporates segmentation by processing different event types through distinct computational pathways or feature extraction mechanisms within the model architecture. This allows the model to maintain specialized pattern recognition capabilities for different event types (e.g., seasonal patterns for weather, temporal patterns for network usage) while still operating as a single integrated system that optimizes resource usage
Data Source
AI summary
Methods, systems and computer program products are provided for prefetching information and/or (pre)allocating computing resources based on predicting classification labels with temporal data. A trained temporal classification model forecasts events (e.g., too numerous for individual modeling) by predicting classification labels indicating whether events will occur, or a number of occurrences of the events, during each of a plurality of future time intervals. Time-series datasets, indicating whether events occurred, or a number of occurrences of the events, during each of a plurality of past time intervals, are transformed into temporal classification datasets. Classifications may be based, at least in part, on extracted features, such as data seasonality, temporal representation, statistical and/or real-time features. Classification labels are used to determine whether to take one or more actions, such as, for example, prefetching information or (pre)allocating a computing resource.