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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Vydáno v:	IEEE access Ročník 13; s. 24158 - 24170
Hlavní autoři:	Noor, Kubra, Fatima, Ubaida
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adaptation models adaptive forecasting approaches advanced feature selection Autoregressive models Data models Datasets Digital currencies dynamic predictive models financial market prediction Forecasting gradient boosting algorithms Learning machine learning in finance Meta-learning techniques Metalearning Predictive models Stock markets Support vector machines Time series analysis
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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