# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 4 Experiment
### Datasets
#### 4.1.2 Foreign Exchange Forecasting Dataset  
For the second dataset, we consolidate data from two distinct Kaggle sources. The first, titled \"Economic Calendar Invest Forex\" ([[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)) serves as an extensive archive of significant economic events, news, and volatility assessments, focusing on the economies of developed nations, especially the United States, Britain, and Japan, due to their profound influence on global forex markets. This dataset includes critical economic indicators and historical data publications. The second source, \"Foreign Exchange Rates 2000-2019\", ([[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)) produced by the Federal Reserve's Data Download Program, features a comprehensive time series of foreign exchange rates per dollar for multiple countries. Key Variables in the Dataset: The primary structure of the dataset utilized in this study is as follows:  
* Foreign Exchange Data:
* Date: The specific date when the foreign exchange rate was recorded. * Japan exchange: The exchange rate of the US dollar against the Japanese yen. * Euro exchange: The exchange rate of the US dollar against the Euro. * UK exchange: The exchange rate of the US dollar against the British pound. * Economic Calendar Data:
* Date: The exact date of the anticipated or actual economic event. * Country: The country associated with the economic event. * Volatility: The expected impact of the event on currency or indexes. * Event: A concise summary of the economic event. * Assessment: Evaluation of the event's outcome compared to forecasts--better, worse, or as expected. * Actual Data: The actual data reported for the event. * Prior Data: Previous data related to the event, with notes on any interim changes. Textual Description Generation: Similar to the Gold-price dataset, the economic calendar data for the Forex dataset are converted into natural language summaries to make them suitable for language models. For example, \"On 2011-01-03, in United States, the 'ISM Manufacturing Prices' was reported. The current value is 72.5, previously it was 69.5. The degree of volatility is Low Volatility Expected, showing a increased of 3.0, as it is considered 'Better Than Expected\". This method transforms raw numerical data into concise, contextual summaries that enhance the dataset's utility for language-based analyses. Data Alignment and Focused Selection: To ensure consistency across datasets, the Forex data are aligned with the dates of economic events, using the same \"no news\" filling approach as in the Gold-price dataset to address dates without significant economic events. Given that the majority of economic events in the calendar dataset are pertinent to the United States, Britain, and Japan, our dataset exclusively incorporates exchange rate data for the euro, yen, and pound to enhance the dataset's relevance and ensure a high correlation with the included economic events. This targeted selection strategy helps to maintain a focused and relevant dataset for analyzing forex fluctuations. Resulting Samples: The combination of foreign exchange rates with textual descriptions of economic events resulted in a dataset of 2347 samples. Each sample contains the following:  
* Date: The trading day to which the forex rates and any economic news pertain. * Forex Rates: The recorded exchange rates for the day.