MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data

MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data

This paper introduces a novel multimodal framework for economic time series forecasting, integrating textual information with historical price data to enhance predictive accuracy. The proposed method employs a multi-head attention mechanism to dynamically align textual embeddings with temporal price...

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Bibliographic Details
Main Authors: Shangyang Mou, Qiang Xue, Jinhui Chen, Tetsuya Takiguchi, Yasuo Ariki
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
economic time series forecasting
feature engineering
multimodal data fusion
deep learning
data mining
Online Access:https://www.mdpi.com/2076-3417/15/3/1241
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