Time-Series Optimized Transformer For Observability With Multimodal Input (TOTO-M)

Abstract

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.

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