Bi-LSTM Agent Model for Accurate Resource Allocation
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Solution Overview
Problem
Existing machine learning models are limited in making accurate predictions for future trends and are unable to handle data unrelated to their training subject matter, leading to inefficient use of resources for remedial actions due to inaccurate predictions.
Innovation Solution
A combination of a bi-directional long-short term memory (Bi-LSTM) model and an agent learning model is used to generate accurate time series forecasting, where the Bi-LSTM model is trained using training data and optimized, and the agent learning model corrects forecasting errors based on actual data to improve prediction accuracy and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used for prediction, then resource allocation can be performed, but prediction accuracy is insufficient leading to inefficient resource use
Solution Approach 1:
The system implements feedback by obtaining actual event data after predictions are made and using this actual data to retrain and update the machine learning model. This closed-loop feedback mechanism allows the model to continuously improve prediction accuracy by learning from real outcomes, thereby reducing resource waste from inaccurate predictions while maintaining efficient resource allocation.
Solution Approach 2:
The system performs preliminary training of the machine learning model using historical training data before actual resource allocation decisions are made. This preliminary preparation ensures the model is as accurate as possible before being deployed for prediction, reducing the need for remedial actions and improving both prediction accuracy and resource efficiency.
2Measurement precision
If machine learning models are retrained frequently to improve accuracy, then prediction accuracy improves, but computational resources and time are consumed
Solution Approach 1:
The system employs periodic retraining where the machine learning model is updated at scheduled intervals or when sufficient new actual data has accumulated, rather than continuously or after every prediction. This periodic approach maintains prediction accuracy through regular updates while avoiding the excessive time and computational resource consumption of continuous retraining.
3Measurement precision
If machine learning models are retrained frequently to improve accuracy, then prediction accuracy improves, but computational resources are consumed
Solution Approach 1:
The system employs periodic retraining where the machine learning model is updated at scheduled intervals or when sufficient new actual data has accumulated, rather than continuously or after every prediction. This periodic approach maintains prediction accuracy through regular updates while avoiding the excessive time and computational resource consumption of continuous retraining.
Solution Approach 2:
The system automatically retrieves actual event data, retrains the model, and updates predictions without requiring manual intervention. This self-service mechanism ensures the model stays accurate while optimizing resource usage by performing retraining only when necessary, based on data availability and performance thresholds.
Data Source
AI summary
In some implementations, a device may generate, using a machine learning model, a first output relating to an event during a first period of time. The machine learning model may be trained using training data. The device may obtain actual data relating to the event during a second period of time that precedes the first period of time. The device may generate updated training data based on the training data and the actual data. The device may train, using the updated training data, the machine learning model to generate an updated machine learning model. The device may generate, using the updated machine learning model, a second output relating to the event during a third period of time. The device may cause one or more resources to be allocated based on the second output.