# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 4 Experiment
### Datasets
#### 4.1.1 Gold Price Forecasting Dataset  
For the first dataset, we utilize a publicly available Kaggle repository entitled \"The Effect of Economic News on Gold Prices\" ([[https://www.kaggle.com/datasets/fekihmea/the-effect-of-economic-news-on-gold-prices/data](https://www.kaggle.com/datasets/fekihmea/the-effect-of-economic-news-on-gold-prices/data)]([https://www.kaggle.com/datasets/fekihmea/the-effect-of-economic-news-on-gold-prices/data](https://www.kaggle.com/datasets/fekihmea/the-effect-of-economic-news-on-gold-prices/data)) (accessed on 23 January 2025)). This dataset comprises two primary components: (i) historical gold-price data spanning from January 2019 to December 2023, capturing daily market dynamics and highlighting the evolving trends that shape gold's valuation; and (ii) an economic calendar detailing significant events over the same period from key global participants--including Canada, Japan, the United States, Russia, the European Union, and China. By integrating these two data streams, the dataset provides a comprehensive perspective on how economic news from these influential regions influences the highly volatile gold market. Key Variables in the Dataset: The primary structure of the dataset utilized in this study is as follows:  
* Historical Gold Prices:
* Date: The specific date corresponding to the recorded gold price. * Price: The daily closing price of gold. * Economic Calendar Data:
* Date: The exact date of the anticipated or actual economic event. * Country: The country associated with the economic event. * Event: A concise label for the specific economic indicator or event (e.g., inflation rate release and unemployment rate). * Actual: The real reported value of the economic indicator for the current event. * Prior: The most recently released value of the same economic indicator for comparison. Textual Description Generation: The economic calendar data, illustrated in Table 1, contain various events, numerical values, and dates. However, these raw data formats are not directly usable by language models, which are designed to process natural language text. To make these data compatible, we generate textual descriptions for each economic event using a standardized template. These descriptions include the event's date, name, the reported value, and its deviation from prior or consensus values, enabling clearer interpretation by language models. For example, \"On 01/01/19, the Caixin Manufacturing PMI for December was reported at 49.70, down from the previous value of 50.20, indicating a decrease of 0.50\". This approach transforms tabular numerical data into concise, natural-language summaries, enhancing the dataset's utility for language-based analyses. Data Alignment and \"No News\" Filling: To integrate textual descriptions with historical gold price data, we align each event-based description to the corresponding trading date in the price dataset. For dates without economic events, we fill the gap with the placeholder phrase \"no news\" to ensure temporal consistency. This approach guarantees that every date in the Gold-price dataset has a corresponding textual entry, maintaining completeness and coherence in the resulting data structure. Resulting Samples: The integration of historical gold prices with textual economic event descriptions produces a dataset of 1352 samples. Each sample consists of the following:  
* Date: The trading day associated with the gold price and any economic news. * Gold Price: The recorded closing price of gold on the specified date. * Economic News Description: A one-sentence summary of the economic event(s) released that day, or the placeholder phrase \"no news\" for days without scheduled announcements. This curated dataset encapsulates the intricate interplay between global economic events and gold price fluctuations, offering a granular perspective on market behavior.