Multi-Modal Time Series and Text Embedding for Anomaly Detection
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Solution Overview
Problem
Time series information from sensors is difficult for human operators to comprehend, making it challenging to identify abnormal behavior in systems, and existing technologies lack effective methods to automatically translate this data into interpretable textual descriptions.
Innovation Solution
A method involving training a neural network with a time series embedding model and a text embedding model using unsupervised and semi-supervised clustering to translate time series and text data into a shared latent space, enabling the transformation of queries into embedded vectors for cross-modal retrieval and automatic explanation of system states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time series data is collected and analyzed to identify abnormal behavior, then system monitoring capability is improved, but the complexity of data interpretation increases
Solution Approach 1:
The patent introduces an intermediary representation layer (embeddings) that translates complex time series data into a simplified latent space representation. This intermediary form bridges the gap between raw sensor data and human interpretation, reducing the complexity of data interpretation while maintaining monitoring capability.
Solution Approach 2:
The patent transforms time series data from its original complex temporal parameters into a different parameter space (latent space) through embedding models. This parameter transformation simplifies the data representation while preserving essential information for abnormal behavior detection.
2Measurement precision
If manual annotation of training data is performed to improve model accuracy, then classification performance is improved, but the time required for data preparation increases
Solution Approach 1:
The patent implements self-service through unsupervised pre-training where the model automatically learns representations from unlabeled time series data. This self-training capability reduces the need for manual annotation while maintaining the quality of learned features, thereby reducing data preparation time without sacrificing classification performance.
Solution Approach 2:
The patent performs preliminary unsupervised learning to pre-train embedding models before final supervised classification. This preliminary action prepares the model with useful representations from unlabeled data, reducing the amount of manually annotated data needed for final training and decreasing overall data preparation time.
3Ease of operation
If multi-modal embedding models are trained to translate time series to text, then interpretability is improved, but the training complexity increases
Solution Approach 1:
The patent segments the training process into distinct phases: unsupervised pre-training for embedding learning and supervised fine-tuning for translation tasks. This segmentation allows the model to first learn robust representations from unlabeled data and then specialize in translation, managing training complexity through structured progression.
Solution Approach 2:
The patent uses partial action by training models on subsets of data and iteratively improving them. The unsupervised pre-training on large unlabeled datasets provides a foundation that reduces the need for extensive supervised training, thereby reducing overall training complexity while maintaining interpretability.
4Measurement precision
If queries are transformed to embedded vectors for cross-modal retrieval, then search accuracy is improved, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary embedding transformations during data ingestion and storage, converting time series data into latent space representations in advance. This preliminary action allows for faster query processing during retrieval operations, as the heavy computational burden of embedding transformation is shifted to the data preparation phase rather than the query phase.
Data Source
AI summary
Methods and systems of training and using a neural network model include training a time series embedding model and a text embedding model with unsupervised clustering to translate time series and text, respectively, to a shared latent space. The time series embedding model and the text embedding model are further trained using semi-supervised clustering that samples training data pairs of time series information and associated text for annotation.