Modular Transformer Encoder for Time Series Domain Adaptation
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Solution Overview
Problem
Machine learning models face challenges in handling time series data due to domain shifts, where changes in data distribution occur over time, affecting the accuracy of predictions.
Innovation Solution
A modular transformer encoder with a policy network is used to adapt an initial prompt to a target domain for time series data, selecting a subset of sub-encoders to process the input time series and adapted prompt, employing few-shot learning for domain adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a standard machine learning model is used for time series data, then the model structure is simple and easy to implement, but the prediction accuracy deteriorates due to domain shifts in data distribution
Solution Approach 1:
The transformer encoder is divided into multiple sub-encoders, each specialized for processing different types of time series data with specific domain characteristics. This segmentation allows the model to handle domain shifts by routing inputs to appropriate sub-encoders, thereby maintaining prediction accuracy while keeping individual sub-encoder structures relatively simple.
Solution Approach 2:
The policy network serves as a universal routing mechanism that directs time series inputs to appropriate sub-encoders based on domain characteristics. This multi-functional component handles both the classification of data domains and the selection of processing pathways, enabling the system to adapt to various domains without requiring completely separate models for each.
2Measurement precision
If domain-specific models are created for each time series domain, then the prediction accuracy for each domain is improved, but the device complexity increases significantly
Solution Approach 1:
Multiple domain-specific sub-encoders are merged into a single unified transformer encoder structure with a shared policy network. This combination allows the system to maintain domain-specific processing capabilities while avoiding the complexity of managing entirely separate models for each domain. The sub-encoders share common infrastructure such as embedding layers and attention mechanisms.
Solution Approach 2:
The model employs dynamic routing through the policy network, which adaptively selects which sub-encoders to activate based on the characteristics of the input time series. This dynamic selection mechanism allows the system to use only the necessary domain-specific components for each input, rather than maintaining all possible domain specializations active simultaneously, thereby reducing effective complexity.
3Adaptability or versatility
If prompt-based meta-learning is applied for domain adaptation, then the adaptability to target domains is improved, but the computational requirements and processing time increase
Solution Approach 1:
The model performs preliminary learning by training the policy network to recognize domain characteristics and select appropriate sub-encoders during the training phase. This preliminary action enables the model to quickly adapt to target domains during inference by simply routing inputs through the pre-trained policy network, rather than performing extensive retraining or adaptation at inference time.
Solution Approach 2:
The prompt-based meta-learning approach creates learned prompt representations that capture domain-specific patterns. These prompt copies can be efficiently applied to new time series inputs without requiring the entire model to be retrained, enabling fast domain adaptation by simply injecting the appropriate prompt representations into the transformer encoder.
Data Source
AI summary
Methods and systems include adapting an initial prompt to a target domain corresponding to an input time series to generate an adapted prompt. The adapted prompt and the input time series are combined. The input time series is processed with the adapted prompt using a modular transformer encoder that has a plurality of sub-encoders, with a policy network selecting a subset of the plurality of encoders that are applied to the input time series and the adapted prompt.