Deep Factor Model with Random Effects for Time Series Forecasting
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Solution Overview
Problem
Traditional time series forecasting methods face challenges in scalability, learning across multiple time series, and addressing the cold-start problem, while neural network-based methods lack proper uncertainty estimation.
Innovation Solution
A global-local forecasting framework, referred to as the Deep Factor Model with Random Effects, combines classical and neural network-based methods, using a global model to capture common patterns and a local model to explain individual variations, enabling efficient inference and uncertainty estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network-based methods are used for forecasting, then the ability to extract patterns across a large number of time series is improved, but the ability to produce proper uncertainty estimation deteriorates
Solution Approach 1:
The patent combines neural network-based methods with Bayesian hierarchical models to create a hybrid forecasting system. The neural networks extract patterns from large numbers of time series while the Bayesian component provides proper uncertainty estimation through probabilistic inference, thus resolving the contradiction between pattern extraction capability and uncertainty estimation accuracy
Solution Approach 2:
The forecasting system uses composite modeling by integrating two different methodological approaches: the pattern recognition strength of neural networks and the probabilistic uncertainty quantification of Bayesian models. This composite approach allows the system to simultaneously achieve both pattern extraction and reliable uncertainty estimation
2Measurement precision
If classical time series forecasting techniques are used, then the ability to capture uncertainty of a small group of time series is improved, but scalability to large numbers of time series deteriorates
Solution Approach 1:
The Bayesian hierarchical model is designed to handle both small and large numbers of time series uniformly. By using shared hyperparameters and hierarchical structures, the model can capture uncertainty for individual series while simultaneously learning from the collective behavior of all series, thus achieving both accuracy and scalability
Solution Approach 2:
The model segments the forecasting problem into hierarchical levels: individual time series level for capturing local uncertainty patterns and a global level for learning shared patterns across all series. This segmentation allows the system to maintain accuracy for individual series while scaling to large numbers of series through efficient information sharing
3Adaptability or versatility
If neural network-based methods are used, then the ability to address the cold-start problem is improved, but the ability to provide proper uncertainty estimation deteriorates
Solution Approach 1:
The Bayesian hierarchical model acts as an intermediary that bridges the neural network's pattern recognition capability with reliable uncertainty estimation. For cold-start scenarios, the hierarchical structure allows the model to borrow strength from related time series through shared priors, providing reliable uncertainty estimates even when individual series have limited historical data
Data Source
AI summary
Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.