แนวคิดหลัก
Modality-aware Transformer enhances financial time series forecasting by leveraging multimodal data sources effectively.
บทคัดย่อ
The article introduces the Modality-aware Transformer model for financial time series forecasting. It addresses challenges in predicting future behaviors of time series linked to external data sources. The model incorporates feature-level attention layers and novel multi-head attention mechanisms to extract valuable insights from diverse data modalities. Extensive experiments demonstrate the superiority of the Modality-aware Transformer over existing methods, offering a practical solution for complex challenges in multimodal financial time series forecasting.
Abstract:
- Financial time series forecasting is challenging due to reliance on external data sources.
- The Modality-aware Transformer model excels in exploring both text and numerical data for accurate predictions.
- Feature-level attention layers enhance the focus on relevant features within each modality.
Introduction:
- Prediction models are crucial for decision-making applications across various sectors.
- Deep learning models, especially transformers, have shown success in time series forecasting.
- Challenges arise when external data sources influence performance more than historical values.
Methodology:
- Problem definition: Multi-step-ahead time series forecasting problem formulation.
- Modality-aware Transformer architecture with intra-modal and inter-modal multi-head attention mechanisms.
- Loss function: Mean squared error (MSE) used as the training objective.
Experimental Setup:
- Evaluation conducted on real-world datasets including U.S. Interest Rates and FED reports.
- Implementation details: Adam optimizer, batch size of 16, and specific configurations for transformer-based models.
Results:
- Extensive experiments show that the Modality-aware Transformer outperforms existing methods across different interest rate maturities.
- Superior performance observed, especially for long-term maturities like 10 years and 30 years.
สถิติ
"Extensive experiments on financial datasets demonstrate that Modality-aware Transformer outperforms existing methods."
"Our proposed model significantly improves inference performance across all interest rate maturities."
คำพูด
"In practice, the key challenge lies in constructing a reliable time series forecasting model capable of harnessing data from diverse sources."
"Our proposed modality-aware structure enables the model to assign different weights for the same timestep in different modalities."