Multi-Modality Transformer for Time-Series Forecasting
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Solution Overview
Problem
Existing time-series forecasting models struggle to effectively integrate and analyze multiple data modalities, such as textual and time-series data, which limits their ability to capture complex relationships and dependencies.
Innovation Solution
A multiple modality aware artificial intelligence neural network transformer is employed, which includes an encoder and a decoder configured to process separate modality streams. The encoder performs feature-level attention, intra-modal multi-head attention, and inter-modal multi-head attention to extract relevant information from both textual and time-series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing time-series forecasting models are used, then the model structure is simple, but the ability to integrate and analyze multiple data modalities is limited
Solution Approach 1:
The model segments different data modalities into separate processing streams (textual data stream and time-series data stream), each handled by dedicated encoder components. This allows the system to process multiple modalities simultaneously while maintaining clear organizational structure and managing complexity through functional separation.
Solution Approach 2:
The transformer architecture serves as a universal processing framework that handles both textual and time-series data through standardized attention mechanisms. The encoder-decoder structure with multi-head attention is applied uniformly across different modalities, enabling the system to process diverse data types with a single versatile architecture.
2Measurement precision
If multiple data modalities are integrated, then the forecasting accuracy is improved, but the complexity of analyzing relationships between modalities increases
Solution Approach 1:
The attention mechanism serves as an intermediary that selectively weighs and integrates information from different modalities. Rather than directly combining all modalities, the attention mechanism mediates the integration process by dynamically determining which features from textual and time-series data are most relevant, simplifying the analysis of inter-modal relationships.
Solution Approach 2:
The model applies different processing characteristics to different modalities through separate encoder streams, with each stream optimized for its specific data type. The attention mechanism then locally integrates relevant features from each modality based on their specific contributions to the forecasting task, rather than applying uniform processing across all modalities.
3Loss of information
If feature-level attention and multi-head attention are performed, then the extraction of relevant information is improved, but the computational complexity increases
Solution Approach 1:
The attention mechanism implements partial action by selectively attending to only the most relevant features and time steps rather than processing all information uniformly. Multi-head attention divides the attention computation into multiple parallel streams, each focusing on different aspects of the data, thereby extracting relevant information more efficiently while distributing computational load.
Data Source
AI summary
A system, computer program product, and method are provided for leveraging artificial intelligence (AI) directed at time-series forecasting. An AI transformer model is configured to support multiple modality datasets for predicting a target time-series together with an explanation through one or more neural attention mechanisms. The multiple modality transformer model exploits intermodal interactions from a first dataset having a first modality, in addition to multi-modality interactions between the first dataset and a second dataset having a second modality different from the first modality.