# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 5 Conclusions  
This study has successfully demonstrated the significant potential of integrating textual data into economic time series forecasting with the proposed MM-iTransformer. We have shown that incorporating textual information markedly enhances forecasting accuracy. Our findings underscore that the inclusion of news text not only aligns with the inherently multimodal nature of economic forecasting but also significantly improves predictive accuracy as evidenced by the notable reduction in MSE for both the Gold-price and Forex datasets.  
However, we acknowledge certain limitations in this study, particularly concerning the scope and synchronization of the datasets used. While our results validate the utility of textual information for forecasting, the relatively limited scope of our datasets may not fully encompass the diverse and region-specific characteristics of global financial markets. Furthermore, the challenge of aligning textual data with time series data is exacerbated by the scarcity of large, well-structured, and temporally synchronized datasets in the financial sector.  
To overcome these limitations, we plan to develop a larger, more comprehensive dataset in future work. This enhanced dataset will aim to better synchronize region-specific and real-time news data with financial time series, providing a more robust basis for evaluating and validating the proposed method. Through these efforts, we hope to further advance the field of multimodal economic forecasting by leveraging a broader spectrum of synchronized data, thereby enriching our understanding and predictive capabilities within this complex domain.  
Conceptualization, S.M. and Q.X.; methodology, S.M.; software, S.M. and Q.X.; validation, S.M., J.C. and Y.A.; formal analysis, J.C. and Y.A.; investigation, S.M.; resources, J.C. and Y.A.; data curation, S.M. and T.T.; writing--original draft preparation, S.M.; writing--review and editing, J.C. and Y.A.; visualization, J.C. and Y.A.; supervision, T.T.; project administration, T.T.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets analyzed during the current study are available in publicly accessible repositories. These datasets can be found at the following URLs: [[https://www.kaggle.com/datasets/devorvant/economic-calendar](https://www.kaggle.com/datasets/devorvant/economic-calendar)]([https://www.kaggle.com/datasets/devorvant/economic-calendar](https://www.kaggle.com/datasets/devorvant/economic-calendar)) (accessed on 23 January 2025), [[https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019](https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019)]([https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019](https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019)) (accessed on 23 January 2025), and [[https://www.kaggle.com/datasets/fekhinea/the-effect-of-economic-news-on-gold-prices/data](https://www.kaggle.com/datasets/fekhinea/the-effect-of-economic-news-on-gold-prices/data)]([https://www.kaggle.com/datasets/fekhinea/the-effect-of-economic-news-on-gold-prices/data](https://www.kaggle.com/datasets/fekhinea/the-effect-of-economic-news-on-gold-prices/data)) (accessed on 23 January 2025).  
The authors declare no conflicts of interest.