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Bridging Univariate Foundation Models and Multivariate Financial Signals for Stock Price Prediction | ||
| Journal of Computing and Security | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 تیر 1404 | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22108/jcs.2025.144738.1160 | ||
| نویسندگان | ||
| Fatemeh Chitsaz* ؛ Saman haratizadeh | ||
| Department of Intelligent Systems, Faculty of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| This paper presents a hybrid forecasting framework that couples a pre-trained univariate foundation model for time-series analysis with a lightweight multivariate predictor to forecast next-day stock prices on the Tehran Stock Exchange. We design two complementary fusion strategies. In the residual-correction strategy, a conventional multivariate regressor first produces a price estimate; its day-ahead error sequence is then modelled by the foundation model, whose predicted residual is added back to refine the original forecast. In the relational strategy, the foundation model generates independent one-step forecasts for each financial variable and these preliminary outputs are concatenated and fed into a multivariate neural network that delivers the final price prediction. The framework is evaluated on ten highly liquid Iranian stocks across the 2000-2023 period. Empirical results show that the hybrid approach lowers Mean Squared Error by as much as 30 percent and raises the macro-averaged F1-score for price-direction classification by up to 25 percent when compared with strong foundation-model baselines. These improvements underscore the value of combining pre-trained temporal representations with auxiliary market signals for more accurate and robust stock forecasting. | ||
| کلیدواژه‌ها | ||
| Time series foundation models؛ stock market prediction؛ TimesFM | ||
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آمار تعداد مشاهده مقاله: 23 |
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