Dual DNN Time-Series Forecasting Uncertainty
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Solution Overview
Problem
Conventional machine learning methods struggle to effectively estimate aleatoric uncertainty in time-series forecasting due to the randomness in underlying data, which hinders the reliability of predictions.
Innovation Solution
The proposed solution involves using a dual Deep Neural Network (DNN) architecture, where a forecasting DNN predicts future time-series points and an uncertainty DNN estimates the aleatoric uncertainty by analyzing the residuals between predicted and actual data, employing components like dense layers, LSTM, and convolutional layers, and training both DNNs concurrently to provide uncertainty metrics such as variance and noise probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning methods are used for time-series forecasting, then the forecasting process is simple, but the ability to estimate aleatoric uncertainty is poor
Solution Approach 1:
The model is segmented into two distinct DNN components: a forecasting DNN that predicts future time-series values and an uncertainty DNN that estimates aleatoric uncertainty. This segmentation allows each component to specialize in its specific function, with the uncertainty DNN focusing exclusively on learning the statistical properties of noise in the data, thereby improving uncertainty estimation accuracy without requiring the entire system to be overly complex.
Solution Approach 2:
The uncertainty DNN acts as an intermediary component that processes the relationship between historical data, forecasted values, and the underlying noise characteristics. By introducing this intermediate layer, the system can explicitly model and estimate aleatoric uncertainty without disrupting the core forecasting functionality, resolving the contradiction between simplicity and uncertainty estimation capability.
2Reliability
If a dual DNN architecture is used to estimate aleatoric uncertainty, then the reliability of predictions is improved, but the computational complexity increases
Solution Approach 1:
The forecasting DNN and uncertainty DNN are merged into a single integrated model that is trained concurrently using a unified loss function. This combination allows both functions to work together synergistically, where the forecasting component provides predictions and the uncertainty component provides confidence measures, improving overall prediction reliability while managing computational complexity through unified training procedures.
Solution Approach 2:
The dual DNN architecture is designed to perform multiple functions: the forecasting DNN generates future value predictions, while the uncertainty DNN estimates aleatoric uncertainty. Both components share the same input data and can be trained simultaneously, making the system multi-functional without requiring separate independent models, thereby improving reliability while controlling complexity.
3Measurement precision
If aleatoric uncertainty estimation is implemented, then the quality of forecast evaluation is improved, but the difficulty of modeling increases
Solution Approach 1:
The patent replaces traditional statistical methods for uncertainty estimation with a neural network-based approach. Instead of using conventional statistical formulas that struggle with complex, non-linear relationships in time-series data, the uncertainty DNN learns the statistical properties of noise directly from data, substituting mechanical statistical calculations with adaptive neural network learning, thereby improving forecast quality evaluation while managing modeling difficulty.
Solution Approach 2:
The model changes the parameters being learned by the uncertainty DNN to focus specifically on the statistical properties of noise (such as variance and distribution characteristics) rather than trying to model the entire time-series generation process. This parameter change simplifies the modeling task by concentrating on uncertainty estimation rather than full data generation, improving forecast evaluation quality while reducing modeling complexity.
Data Source
AI summary
Systems and methods for receiving a time-series that includes a historical or current observation and determining future points of the time-series utilizing a forecasting deep neural network (DNN) to analyze the time-series and determining an uncertainty of the future points utilizing an uncertainty DNN to analyze the time-series and future points. The output would include providing the future points of the time-series and the uncertainty data. The steps further include training the forecasting DNN with historical data and training the uncertainty DNN with the trained forecasting DNN utilizing a residual of an estimate from the forecasting DNN and actual data.