Double Descent Training for Time Series Forecasting
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Solution Overview
Problem
Current deep learning models for time series forecasting are undertrained, leading to suboptimal performance, as they do not leverage the 'double descent' phenomenon, which suggests that increased model complexity and training epochs can improve generalization performance beyond initial overfitting.
Innovation Solution
A 'modern regime' training framework for deep learning-based time series forecasting models is introduced, which increases model complexity and extends training epochs beyond conventional limits, allowing the model to experience a 'double descent' by initially improving, then deteriorating, and finally exceeding the initial performance maximum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model complexity and training epochs are increased, then generalization performance can exceed initial local maximum, but training time and computational resources increase
Solution Approach 1:
The method proactively extends training beyond the point where validation performance first deteriorates, anticipating the double descent phenomenon. By pre-planning to train through the overfitting phase and into the second descent, the system prepares for improved generalization performance before it naturally occurs, rather than stopping at the first sign of validation loss increase.
Solution Approach 2:
The method changes the training parameter (number of epochs) from the conventional stopping point to a point beyond the first validation maximum. This parameter change allows the model to traverse the double descent curve and reach the second, lower validation loss region, thereby improving generalization performance despite increased training time.
2Reliability
If training epochs are extended beyond initial performance maximum, then performance can exceed local maximum, but risk of overfitting increases
Solution Approach 1:
The method converts the harmful effect of overfitting (validation loss increase) into a beneficial outcome. By intentionally allowing training to continue through the overfitting phase, the system exploits the double descent phenomenon where the model eventually generalizes better than at the first maximum, transforming what was traditionally seen as a harmful overfitting region into a productive training zone.
Solution Approach 2:
The system proactively anticipates and prepares for the double descent phenomenon by extending training epochs before validation performance deteriorates. This preliminary action allows the model to naturally traverse the overfitting region and reach the second descent, converting the potential harm of overfitting into the benefit of improved generalization.
3Adaptability or versatility
If model complexity is increased, then capacity to learn complex patterns improves, but computational resources and training time increase
Solution Approach 1:
The method changes the training parameter (number of epochs) rather than increasing model architecture complexity. By extending training time, the existing model capacity is fully utilized to achieve better generalization, avoiding the need for more complex models with additional parameters or layers, thereby managing computational resources more efficiently.
Data Source
AI summary
A “modern regime” training framework for deep learning-based time series forecasting models is described herein. In summary, the “modern regime” training framework consists of configuring the hyperparameters of the training process in a manner that substantially increases the complexity of the time series forecasting model beyond conventional norms and enables the time series forecasting model to achieve a “double descent” during the training process. In essence, a “double descent” has occurred when, after initially improving and reaching a local maximum performance, the performance of the time series forecasting model is allowed to deteriorate substantially and then improve again to reach a performance that exceeds the initial local maximum performance.