Time Series Encoder Coupled with Text Encoder for Semantic Analysis
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Solution Overview
Problem
Existing foundation models cannot efficiently analyze time series of measurements due to limited annotated training data, as time series data are typically not accompanied by captions but only by single keywords and meta information.
Innovation Solution
A computer-implemented method generates a data-driven model for analyzing time series of measurements by processing multiple time series data sets with textual meta data, using a Large Language Model to generate phrases for each time series, and training a time series encoder coupled with a pre-trained text encoder to create a semantic representation space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If foundation models are trained on time series data with only single keywords and meta information, then the model can be trained with available data, but the training data quality and annotation richness are insufficient
Solution Approach 1:
A large language model is introduced as an intermediary to generate rich textual phrases and descriptions from sparse time series metadata. This intermediary transforms limited keywords and meta information into comprehensive training annotations, enabling foundation models to learn from adequately described time series data without requiring manual captioning of each dataset.
2Loss of information
If manual captioning is performed for each time series dataset, then training data quality improves, but the time and resources required for data preparation increase significantly
Solution Approach 1:
The system enables self-service annotation by automatically generating comprehensive textual descriptions of time series datasets using large language models. Instead of requiring manual human annotation of each dataset, the system autonomously creates rich training annotations from available metadata, dramatically reducing data preparation time while maintaining high annotation quality.
Solution Approach 2:
The large language model generates training phrases and descriptions in advance during the data preparation phase, creating a rich annotated dataset before foundation model training begins. This preliminary generation of annotations eliminates the need for time-consuming manual captioning during or after the training process.
3Adaptability or versatility
If existing foundation models are applied to time series data, then model availability is maintained, but the ability to effectively analyze and understand time series semantics is limited
Solution Approach 1:
The patent transforms the input parameters and representation format of time series data by generating rich textual phrases and semantic descriptions that accompany the numerical measurements. This parameter transformation enables existing foundation models to process time series data with the same semantic understanding capabilities they possess for textual data, significantly improving semantic analysis accuracy while maintaining model availability.
Data Source
AI summary
A method for generating a data driven model for analyzing time series of measurements, wherein the method processes time series data sets, each time series data set including a time series of measurements and textual meta data describing the time series of measurements. The method includes i) generating for each time series of measurements several phrases by processing the meta data describing the respective time series of measurements, resulting in training data sets; and ii) training a time series encoder based on the training data sets, the time series encoder being a neural network for generating in a representation space a semantic representation of an input time series of measurements fed to the time series encoder, where the time series encoder is coupled with a text encoder, the text encoder generating in the representation space a semantic representation of an input phrase fed to the text encoder.