Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts

Wenfa Wu, Guanyu Zhang, tan zheng, Yi Wang, Hongsheng Qi

Keywords: time series forecasting, multimodal time series model, multimodal comprehension

Abstract: Time series forecasting plays a vital role for decision-making across a wide range of real-world domains, which has been extensively studied. Most existing single-modal models rely solely on numerical series, which suffer from the limitations imposed by insufficient information. Recent studies have revealed that multimodal models can address the core issue by integrating textual information. However, these models focus on either historical or future textual information, overlooking the unique contributions each plays in time series forecasting. Besides, these models fail to grasp the intricate relationships between textual and time series data, constrained by their moderate capacity for multimodal comprehension. To tackle these challenges, we propose Dual-Forecaster, a pioneering multimodal time series model that combines both descriptively historical textual information and predictive textual insights, leveraging advanced multimodal comprehension capability. We begin by developing the historical text-time series contrastive loss to align the descriptively historical textual data and corresponding time series data, followed by encoding multimodal text-time series representations between them through the history-oriented modality interaction module, and then combining predictive textual data through the future-oriented modality interaction module to ensure textual insights-following forecasting. Our comprehensive evaluations on synthetic dataset and captioned-public datasets demonstrate that Dual-Forecaster is a distinctly effective multimodal time series model that outperforms or is comparable to other state-of-the-art models, highlighting the superiority of integrating textual information for time series forecasting. This work opens new avenues in the integration of textual information with numerical time series data for multimodal time series analysis.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 8603