Stock Market Prediction Using Meta-Learning and Few-Shot Learning

Stock market predicting is a difficult because of its constant fluctuations and intricate nature. Traditional machine learning models typically rely on extensive labelled data to effectively capture market trends and generate precise predictions. So this approach is not suitable when faced with unfamiliar market conditions, where historical data is scarce or non-existent. To solve this problem, we have to develop meta-learning and few-shot learning methods. These methods show great potential for improving the ability of models to generalize from limited data.By meta-learning it is possible to predict price movements, volatility forecasting, or trend analysis, without the need for extensive retraining. For this purpose we use the frameworks model-agnostic meta-learning (MAML) and Siamese networks. This paper examines usage of Meta-Learning and Few-Shot learning method for predicting stock market trends, emphasizing their capacity to effectively handle limited data and adapt to changing market dynamics. We also explore the possibilities of this approach to enhance the accuracy, resilience, and adaptability of financial modelling.