Time Series Forecast Model Feature Visualization
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Solution Overview
Problem
Current time series models lack the ability to visually represent the importance of each feature on future forecasts, hindering users' understanding of how individual features affect predictions.
Innovation Solution
A method for visualizing time series models is developed, which includes obtaining a time series dataset, generating a future forecast model using a machine learning model with a time series forecaster, and displaying the model to users. The visualization includes a future prediction and feature visualization outputs that illustrate the effects of selected forecasting model features on the prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional time series models are used to generate future forecasts, then forecasting accuracy is achieved, but the models lack the ability to visualize feature importance and interpretability
Solution Approach 1:
The patent segments the complex time series model into multiple visualizable components, including feature importance plots, component contribution charts, and prediction breakdown visualizations. Each component is separately analyzed and displayed to provide comprehensive interpretability without requiring users to understand the entire complex model structure.
Solution Approach 2:
The patent introduces visualization tools as intermediary elements between the complex machine learning model and the end user. These visualizations act as mediators that translate complex model internals into comprehensible graphical representations, allowing users to understand feature importance without directly interacting with the complex model architecture.
2Ease of operation
If multiple forecasting model features are analyzed to improve understanding, then interpretability is enhanced, but the visualization complexity and computational requirements increase
Solution Approach 1:
The patent implements dynamic and interactive visualizations that adapt to user selections and interactions. Users can dynamically select different features, time periods, and visualization types, and the system responds by updating the displays in real-time. This dynamic approach allows comprehensive analysis of multiple features while keeping the interface manageable through on-demand generation of visualizations.
3Reliability
If comprehensive feature analysis is provided in the forecast model, then decision-making quality is improved, but the time and computational resources required for model generation increase
Solution Approach 1:
The patent performs preliminary computations of feature importance and component contributions during the model training phase, storing these results for later visualization. By pre-calculating and caching these interpretability metrics, the system enables rapid generation of comprehensive visualizations during inference without requiring additional computational resources at prediction time.
Data Source
AI summary
A method for a time series forecaster including a machine learning (ML) model and a time series forecasting algorithm may be used to generate a future forecast model for a time series dataset. The future forecast model includes a future prediction showing a future forecast of the time series dataset. Rather than simply presenting the importance of a forecasting model feature in a limited manner, such as in the form of a number (e.g., a float number), the future forecast model of one or more embodiments may advantageously include at least one forecasting model feature visualization output that visually illustrates the effects of one or more forecasting model features on the future prediction.