THE POTENTIAL OF HYBRID LSTM-GENERATIVE AI ECO-MODEL IN FORECASTING FINANCIAL AND ECONOMIC INDICATORS
This study presents the development and evaluation of The Hybrid LSTM- Generative AI ECO-Model for forecasting financial and economic indicators such as EUR/USD exchange rate, utilizing a combination of Long Short-Term Memory (LSTM) networks and generative AI models (GPT-2 and Llama- 3.2-1B). Th...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Oikos Institute - Research Center Bijeljina
2025-07-01
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| Series: | Collection of Papers New Economy |
| Subjects: | |
| Online Access: | https://conference.oikosinstitut.org/files/proc/Vol3No1/13.pdf |
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| Summary: | This study presents the development and evaluation of The Hybrid LSTM-
Generative AI ECO-Model for forecasting financial and economic indicators
such as EUR/USD exchange rate, utilizing a combination of Long Short-Term
Memory (LSTM) networks and generative AI models (GPT-2 and Llama-
3.2-1B). The primary objective was to achieve high prediction accuracy while
minimizing computational resource consumption and ensuring ease of use of
the model on various devices. The model was trained and tested on historical
financial and economic data, including exchange rates, macroeconomic
indicators, commodity prices, and sentiment analysis of financial news.
Our findings indicate that traditional LSTM models outperform generative AI
models in time-series forecasting tasks due to their ability to capture temporal
dependencies. However, integrating generative AI for dataset refinement
and model optimization significantly improved forecasting performance. The
hybrid ECO-model, leveraging generative AI-driven parameter selection and
sentiment analysis, demonstrated superior accuracy for long-term predictions.
The most influential parameters included historical exchange rate trends, gold
and oil prices and news sentiment.
By implementing an ECO-approach that optimizes dataset size, minimizes
training iterations, and employs a lightweight model architecture, our study
highlights a path toward efficient and sustainable financial forecasting. Future
research directions include enhancing anomaly detection mechanisms,
incorporating additional weak predictors, and refining the role of generative
AI in hybrid time-series forecasting models. |
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| ISSN: | 2831-1728 2831-1736 |