Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets
This research proposes a Meta learning framework for financial time series forecasting, designed to rapidly adapt to novel market conditions with minimal retraining. The framework operates in two stages: 1) pretraining on a diverse set of financial datasets, including stocks (e.g., MSFT, AAPL) and c...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10795129/ |
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