Language Embeddings for Time Series Classification With Frozen LLMs
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Solution Overview
Problem
The use of large language models for time series classification is computationally expensive due to their large model size, making them unsuitable for resource-constrained environments, and fine-tuning partially frozen pre-trained models involves millions of trainable parameters.
Innovation Solution
A method and system that utilizes a language embedding model to embed time series, pairs the embeddings with a classification head comprising a CNN and an MLP, and applies AI/ML models to achieve high-performance and efficient time series classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used for time series classification, then classification accuracy is improved, but computational cost and model size increase significantly
Solution Approach 1:
The patent segments the large language model into two distinct components: a pre-trained language embedding model (frozen) and a smaller classification head (trainable). This segmentation allows the system to leverage the representational power of large models while keeping the trainable parameter count low, thus improving classification accuracy without proportionally increasing computational cost.
Solution Approach 2:
The patent extracts only the essential feature extraction capability from the large language model by using it solely as an embedding generator. The complex classification logic is removed and replaced with a lightweight classification head, thereby maintaining accuracy while reducing the computational burden of the overall system.
2Reliability
If pre-trained LLMs are fine-tuned for time series tasks, then performance is improved, but the number of trainable parameters increases to millions
Solution Approach 1:
The patent extracts only the necessary classification functionality from the pre-trained LLM by creating a separate classification head. This allows the system to fine-tune only the essential components needed for the specific time series task, reducing trainable parameters from millions to a manageable number while preserving model performance.
Solution Approach 2:
The patent applies local quality by making different parts of the system have different training statuses: the language embedding model remains frozen (pre-trained weights), while only the classification head is trainable. This localized approach to parameter training optimizes the balance between performance and computational efficiency.
3Measurement precision
If complex deep learning models are used for time series classification, then accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent segments the training process into two phases: (1) using a frozen pre-trained language model for feature extraction, and (2) training only a lightweight classification head. This segmentation dramatically reduces training time compared to training entire complex models from scratch or fine-tuning large portions of pre-trained models.
Solution Approach 2:
The patent performs preliminary action by using a pre-trained language model to generate embeddings before the actual classification training. This pre-computed embedding layer provides high-quality features that accelerate the subsequent classification training process, reducing overall training time while maintaining accuracy.
Data Source
AI summary
A method and a system for leveraging language embeddings for time series tasks are provided. The method includes: receiving time series data; generating a standardized time series by formatting the time series data into a standard format; generating text embeddings from the standardized time series by transforming a sample of the standardized time series into an embedding; generating a combined representation by combining the generated text embeddings with the time series data; pairing a classification head framework with the combined representation; and generating a vector representation of time series probabilities based on the pairing of the combined representation with the classification head framework. The classification head framework includes a convolutional neural network (CNN) and a multilayer perceptron (MLP).