Multimodal Time-Series Transformer for Sparse Infrastructure Forecasting
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Solution Overview
Problem
Existing time-series forecasting models struggle with scalability and generalization across diverse datasets, particularly when handling high cardinality, high time resolution, sparsity, right skew, and outliers in infrastructure data, leading to poor performance.
Innovation Solution
A forecasting foundation model incorporating a factorized transformer architecture with space-time attention blocks and a probabilistic mixture model head, such as the Student-T mixture model, to generate accurate probabilistic predictions from multivariate time-series data, including query text embeddings to enhance forecasting in cold-start situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are trained for each metric, then model accuracy for specific metrics can be optimized, but device complexity and maintenance burden increase significantly
Solution Approach 1:
The patent combines multiple metric-specific models into a single unified foundation model that can process and forecast diverse time-series metrics simultaneously. This consolidation reduces the number of separate models from potentially dozens to one, while maintaining the ability to handle different metric types through a shared architecture with metric-specific adaptation layers.
Solution Approach 2:
The foundation model is designed with universal capabilities to handle multiple types of metrics (infrastructure, application performance, business metrics) through a single model instance. The model incorporates metric-type classification and adaptive processing that allows it to generalize across different metric characteristics without requiring separate specialized models for each metric type.
2Device complexity
If basic forecasting models are used, then model simplicity is maintained, but the ability to handle high cardinality, high time resolution, sparsity, and outliers in infrastructure data is insufficient
Solution Approach 1:
The foundation model employs dynamic parameter adaptation that changes model behavior based on input data characteristics. When detecting high cardinality, the model adjusts its embedding dimensions and attention mechanisms. For high time resolution data, it modifies its temporal processing windows. For sparse data, it activates imputation mechanisms, and for outliers, it applies robust loss functions and anomaly detection layers.
Solution Approach 2:
The model incorporates dynamic components that adapt its architecture and processing in real-time based on the characteristics of the input time-series data. This includes dynamic batch size adjustment, adaptive learning rates, and conditional computation paths that activate different processing strategies depending on whether the data exhibits high cardinality, sparsity, or other challenging characteristics.
3Adaptability or versatility
If existing foundation models are used, then general capabilities are improved, but performance deteriorates when handling time-series data with high cardinality, high time resolution, sparsity, and outliers
Solution Approach 1:
The foundation model implements local quality by having different processing mechanisms for different data characteristics within the same model architecture. It uses metric-type-specific embedding layers, conditional attention mechanisms that adapt to data sparsity levels, and specialized heads for handling outliers versus normal data points, allowing each part of the model to be optimized for its specific function while maintaining overall universality.
Data Source
AI summary
The present disclosure describes technology for training and deploying time-series optimized transformers for observability with multimodal input (TOTO-M). The system includes processors and a storage device for storing instructions. The processors may execute the instructions to process multimodal data using an artificial intelligence (AI) model. The AI model includes a text embedding model configured to generate one or more query text embeddings based one or more query texts corresponding to multivariate time-series data The AI model further includes a patch embedding layer configured to generate patch embeddings from the multivariate time-series data and a transformer architecture comprising one or more segments including space-wise blocks and time-wise blocks. The transformer architecture is configured to receive the patch embeddings combined with the one or more query text embeddings, process the patch embeddings, and output transformed embeddings.