Forecast Explanation LLM Evaluation Using Simulated Time Series
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Solution Overview
Problem
Evaluating the accuracy of explanations generated by forecast explainer neural networks in time series forecasting is challenging due to the scarcity of performance metrics that consider complex causal relationships.
Innovation Solution
Implementing systems and methods to evaluate the simulatability of forecast explanations using direct and synthetic simulatability approaches, where the generated explanations are used to generate simulation data, and comparing the results to determine the accuracy of the forecast explainer LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If forecast explainer LLMs are used to generate explanations for time series forecasts, then the interpretability of forecasts is improved, but the difficulty of evaluating accuracy increases due to scarcity of performance metrics
Solution Approach 1:
The patent introduces a simulator as an intermediary component that bridges the gap between forecast explanations and ground truth. The simulator takes forecast explanations as input and generates simulated time series data, which can then be compared against actual time series data to evaluate explanation accuracy. This intermediary approach enables the creation of evaluation metrics without requiring direct access to complex causal relationships in the original data.
Solution Approach 2:
The patent creates synthetic copies of time series data through the simulator, which replicate the causal relationships and patterns present in actual time series data. By working with these synthetic copies rather than the original complex data, the system can evaluate forecast explanations using simplified surrogate tasks that are easier to measure and interpret, while still maintaining fidelity to the original data's causal structure.
2Measurement precision
If complex causal relationships in time series data are considered for evaluation, then the measurement precision of forecast explanation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent extracts the evaluation function from the complex time series data itself and relocates it to the simulator component. By taking out the evaluation logic and implementing it through the simulator's ability to generate and compare synthetic data, the system achieves precise measurement of forecast explanation accuracy without requiring direct analysis of the complex causal relationships in the original time series data.
Solution Approach 2:
The patent uses synthetic, disposable simulated time series data instead of permanently storing and analyzing complex real time series data for evaluation purposes. The simulator generates fresh synthetic data on-demand for each evaluation, which simplifies the evaluation system by avoiding the need to manage and process the complexity of actual time series data while maintaining sufficient precision through the synthetic data's representation of causal relationships.
Data Source
AI summary
Embodiments described herein provide a method for time series forecast. The method includes: obtaining a set of time series data comprising a first segment of past time series data and a second segment of predicted time series data; generating, by a first neural network based language model, a text description describing a forecast explanation based on a first input prompt combining the set of time series data; generating, by a second neural network based language model, a third segment of predicted time series data based on a second input prompt combining the first segment of past time series data and the text description of forecast explanation; determining a performance metric based on a comparison between the second segment of predicted time series data and the third segment of predicted time series data; and generating a control command based on the text description to cause an action with a control system.