Multimodal Foundation Model for Contextual Time Series Forecasting
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Solution Overview
Problem
Existing approaches for predicting future values in time series data lack understanding of the underlying causes of patterns, failing to incorporate contextual information effectively.
Innovation Solution
A multimodal foundation model is trained on time series data and time-stamped contextual information, such as news headlines, to learn interrelations and is then fine-tuned for specific prediction tasks, enabling rapid adaptation with minimal additional data and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing approaches are used for predicting future values in time series data, then prediction capability is achieved, but understanding of underlying causes and contextual information is lost
Solution Approach 1:
The patent combines time series data with exogenous contextual data (text, images, audio, video) into a unified multimodal input framework. The encoder processes multiple data types simultaneously, merging them into a shared latent representation that preserves both temporal patterns and contextual information for more reliable predictions
Solution Approach 2:
The foundation model is designed as a universal architecture that can process multiple modalities (time series, text, images, audio, video) through a single unified encoder-decoder framework. This multi-functional model adapts to different prediction tasks by fine-tuning on task-specific data while maintaining the ability to process diverse input types
2Adaptability or versatility
If a foundation model is trained on multiple modalities, then adaptability to different prediction tasks is improved, but training time and computational resources increase
Solution Approach 1:
The patent pre-trains a universal foundation model on large-scale multimodal data before fine-tuning for specific tasks. This preliminary training establishes a robust multi-modal understanding that can be rapidly adapted to different prediction tasks through fine-tuning, reducing the time needed for task-specific model development
Solution Approach 2:
The model employs dynamic fine-tuning mechanisms that adapt the training process based on the specific task requirements. The architecture allows selective activation of different modalities and adjustment of processing depth depending on the prediction task, optimizing computational resources while maintaining versatility
3Adaptability or versatility
If a foundation model is trained on multiple modalities, then adaptability to different prediction tasks is improved, but computational resources required increase
Solution Approach 1:
The patent segments the processing of different modalities through separate encoder modules that feed into a shared decoder. This segmentation allows efficient resource utilization by processing each modality through optimized pathways while sharing computational resources in the latent space and output layers, reducing overall computational burden
Data Source
AI summary
A foundation model is trained on time series-related data. The foundation model is configured to take as input time series data (e.g., asset prices in a market, power demand in a power grid, scores in baseball games, etc.) as well as related time-stamped exogenous data having a different modality from the time-series data (e.g., news headlines). Once the foundation model is trained, it may be fine-tuned for different decoder heads to make predictions for a range of time series values.