Modality-aware Transformer for Financial Time series Forecasting

• URL: http://arxiv.org/abs/2310.01232v2
• Date: Wed, 20 Mar 2024 21:48:05 GMT
• Title: Modality-aware Transformer for Financial Time series Forecasting
• Authors: Hajar Emami, Xuan-Hong Dang, Yousaf Shah, Petros Zerfos,
• Abstract summary: We introduce a novel multimodal transformer-based model named the textitModality-aware Transformer. Our model excels in exploring the power of both categorical text and numerical timeseries to forecast the target time series effectively. Our experiments on financial datasets demonstrate that Modality-aware Transformer outperforms existing methods.
• Score: 3.401797102198429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}. Our model excels in exploring the power of both categorical text and numerical timeseries to forecast the target time series effectively while providing insights through its neural attention mechanism. To achieve this, we develop feature-level attention layers that encourage the model to focus on the most relevant features within each data modality. By incorporating the proposed feature-level attention, we develop a novel Intra-modal multi-head attention (MHA), Inter-modal MHA and Target-modal MHA in a way that both feature and temporal attentions are incorporated in MHAs. This enables the MHAs to generate temporal attentions with consideration of modality and feature importance which leads to more informative embeddings. The proposed modality-aware structure enables the model to effectively exploit information within each modality as well as foster cross-modal understanding. Our extensive experiments on financial datasets demonstrate that Modality-aware Transformer outperforms existing methods, offering a novel and practical solution to the complex challenges of multi-modal financial time series forecasting.

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