Time-Series-Language Model Tokenization for Domain Adaptation
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Solution Overview
Problem
Existing AI models are limited in their ability to effectively integrate and interpret time-series data using natural language, failing to recognize the connection between time-series and natural language, which is crucial in various domains.
Innovation Solution
A time-series-language (TSLa) model is trained using an encoder-decoder neural network to tokenize time-series data, learn a linear mapping function, and perform token augmentation, allowing it to predict next tokens and adapt to domain-specific tasks through fine-tuning with a domain-specific dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If singular modality focused models are used, then the model structure is simple, but the model is limited to the modality that it is trained with and cannot integrate time-series data with natural language
Solution Approach 1:
The patent merges time-series data and natural language text data into a unified embedding space through a shared encoder-decoder architecture. The encoder processes both modalities and projects them into a common latent space, while the decoder generates outputs in both time-series and text formats, enabling the model to handle multiple modalities simultaneously without requiring separate specialized models for each modality.
Solution Approach 2:
The model achieves universality by designing a single architecture that can perform multiple functions: generating time-series data from text, generating text descriptions from time-series data, and processing both modalities in parallel. The shared encoder-decoder structure serves all these functions, eliminating the need for separate specialized models and enabling flexible adaptation to different task requirements.
2Reliability
If time-series data is tokenized and mapped to language embeddings, then the model can predict next tokens and interpret time-series data, but the training process becomes complex requiring multiple stages
Solution Approach 1:
The training process is segmented into distinct stages: first training the encoder-decoder model on time-series data to learn temporal patterns, then training on natural language text data to learn linguistic representations, and finally fine-tuning on combined data with domain-specific tasks. This segmentation allows each stage to focus on specific learning objectives while building upon previous knowledge, improving overall data efficiency and model performance.
Solution Approach 2:
The model performs preliminary actions by pre-training on large datasets of time-series data and natural language text before fine-tuning on domain-specific tasks. This pre-training establishes robust foundational representations that can be efficiently adapted to specific domains, reducing the need for extensive retraining and improving convergence during domain-specific fine-tuning.
3Adaptability or versatility
If the model is fine-tuned with domain-specific datasets, then the model adapts to perform domain-specific tasks, but the computational resources and training time increase
Solution Approach 1:
The fine-tuning process uses partial action by training only on the essential domain-specific data needed for the target task rather than retraining on all available data. The model leverages the pre-trained representations and only updates parameters necessary for domain adaptation, significantly reducing computational resource consumption while maintaining effective domain-specific performance.
Data Source
AI summary
Systems and methods for training a time-series-language (TSLa) model adapted for domain-specific tasks. An encoder-decoder neural network can be trained to tokenize time-series data to obtain a discrete-to-language embedding space. The TSLa model can learn a linear mapping function by concatenating token embeddings from the discrete-to-language embedding space with positional encoding to obtain mixed-modality token sequences. Token augmentation can transform the tokens from the mixed-modality token sequences with to obtain augmented tokens. The augmented tokens can train the TSLa model using a computed token likelihood to predict next tokens for the mixed-modality token sequences to obtain a trained TSLa model. A domain-specific dataset can fine-tune the trained TSLa model to adapt the trained TSLa model to perform a domain-specific task.