# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
However, most existing approaches simplify the complexity of textual data by focusing on specific aspects, such as sentiment polarity, tone, or keyword-based representations, neglecting the rich, nuanced information embedded in the full text. This narrow scope limits their ability to capture the non-linear and qualitative impacts of news and other unstructured data sources. Incorporating comprehensive news content into financial forecasting can enhance datasets by introducing additional contextual information,improving the adaptability and accuracy of predictive models. Such an approach allows models to better reflect real-world dynamics, capturing the multifaceted interactions that drive market behavior. Therefore, a more comprehensive framework is required to address these challenges, leveraging both unstructured textual data and structured time series to establish a robust and realistic forecasting methodology. This paper introduces a novel approach to economic time series forecasting that integrates textual information directly into the predictive framework as an additional variable. Unlike sentiment-centric methods, this approach fully leverages the richness of textual data, such as economic news and reports, to extract predictive signals absent from numerical data alone. By treating textual data as an extension of time series variables, the proposed method provides a unified framework for modeling the interplay between textual and numerical inputs. The primary contributions of this study are as follows:  
1. Multimodal Attention-Based Integration: The proposed method employs an inverted attention mechanism to directly model interactions between textual data and multivariate time series. By treating text as an additional variable, the model captures intricate relationships between historical price movements and textual information, enabling more accurate and robust economic forecasts. 2. Seamless Integration of Textual and Numerical Data: This study utilizes matched historical price and news datasets to construct multimodal datasets. By combining the contextual depth of textual data with the temporal patterns of price data, the proposed method enhances forecasting performance. 3. Comprehensive Experimental Validation: Extensive experiments on economic datasets demonstrate the effectiveness of the proposed approach. Results highlight the model's ability to outperform traditional forecasting techniques by leveraging both textual and numerical inputs, offering deeper insights into market dynamics. Figure 1 compares (b) our multimodal learning method with the existing (a) unimodal approach, demonstrating how this integration enhances the model's ability to utilize diverse data sources and extract valuable insights, thereby improving the accuracy of time series forecasting  
Figure 1: Time series forecasting framework. The remainder of this paper is organized as follows: Section 2 provides a detailed exploration of existing methods in time series forecasting. Section 3 describes the proposed method and outlines the framework in detail. Section 4 discusses the dataset, presents the experimental results, and explores some notable findings. Finally, Section 5 concludes the paper.