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2023

Modality-aware Transformer for Financial Time series Forecasting

Emami, Hajar, Dang, Xuan-Hong, Shah, Yousaf et al.

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Time series forecasting presents a significant challenge, particularly when its accuracy relies on external data sources rather than solely on historical values.

This issue is prevalent in the financial sector, where the future behavior of time series is often intricately linked to information derived from various textual reports and a multitude of economic indicators.
In practice, the key challenge lies in constructing a reliable time series forecasting model capable of harnessing data from diverse sources and extracting valuable insights to predict the target time series accurately.
In this work, we tackle this challenging problem and introduce a novel multimodal transformer-based model named the \textit{Modality-aware Transformer}.

Modality-aware Transformer for Financial Time series Forecasting · Around

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