# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data  
[PERSON]  
1 Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan; [EMAIL_ADDRESS](Q.X. ); [EMAIL_ADDRESS](T.T. ); [EMAIL_ADDRESS](Y.A. )1 Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan; [EMAIL_ADDRESS](Q.X. ); [EMAIL_ADDRESS](T.T. ); ariki@kobe-u. the application of these models. Firstly, financial data are highly volatile and can exhibit extreme value changes, which undermines the assumption of stable patterns over time [8]. Secondly, compared to other types of time series, financial data typically exhibit more complex seasonality and more variable trends [9]. This variability is often exacerbated by sudden and unpredictable events, adding a layer of complexity to the forecasting. These factors make economic forecasting particularly challenging. Different approaches for economic time series forecasting have been explored, ranging from econometric theory and statistical methodologies to artificial intelligence models. Statistical methods, such as the autoregressive integrated moving average mbox(ARIMA) model [10; 11] and the semi-functional partial linear model [12], have also been employed in this field. These models benefit from a solid mathematical foundation and offer strong interpretability [13], but their predictions are often imprecise due to the nonlinear and irregular nature of economic data. In response to the complexity of economic time series, neural networks and support vector machines (SVM) have demonstrated considerable success in modeling non-linear relationships, offering robust alternatives to traditional linear models [14]. Furthermore, recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have become particularly prevalent due to their ability to effectively extract time dependencies from time series data, thanks to the memory features obtained from their feedback mbox mechanisms [15; 16]. More recently, transformer-based models have emerged for this task, employing attention mechanisms capable of learning global context and long-term dependencies, thus enhancing the depth of time series analysis [17; 18]. As deep learning expands its application in time series forecasting, the integration of natural language processing (NLP) techniques is increasingly transforming economic predictions. Textual data, available in real-world economic time series forecasting applications, often contain nuanced information that may surpass the insights provided by numerical financial series alone [19]. Recent studies have begun using textual data in economic forecasting models to capture these additional insights. For instance, some models analyze large volumes of financial news to identify sentiments and factual information that may affect investor behavior and market trends [20]. In financial market forecasting, [PERSON] et al. [21] propose generating sentiment-based features over time and combining them with time series data to improve predictions. Similarly, [PERSON] et al. [22] focus on agricultural commodity prices, using a low-dimensional representation of domain-specific text data enriched with selected keywords, which addresses issues of high dimensionality and data sparsity, improving forecast accuracy. [PERSON] et al. [23] take a different approach, developing a tone shock measure from European Central Bank communications to predict monetary policy decisions. In cryptocurrency forecasting, [PERSON] et al. [24] apply machine learning methods such as SVR, OLS, ensemble learning, and MLP to examine how macroeconomic, microeconomic, technical, and blockchain indicators relate to Bitcoin prices over short and long terms. These studies show how NLP can enhance time series forecasting by integrating textual data with traditional economic indicators. These studies demonstrate the potential of natural language processing (NLP) to enhance time series forecasting by incorporating textual data alongside traditional economic indicators.