#AIImpactOnForex AI transfer learning for forex (foreign exchange) predictions involves taking a pre-trained machine learning model, typically trained on a different but related task, and adapting it to make predictions on forex market data. The idea is to leverage the knowledge acquired by the model from one domain and apply it to another domain with limited data or less extensive training. This approach is particularly useful in forex predictions, where historical data can be sparse or noisy, and training a model from scratch might be resource-intensive. In this context, a model pre-trained on financial data, stock market trends, or even other time-series forecasting tasks can be fine-tuned on specific forex data, such as currency pairs' price movements. By transferring the learned features or patterns, the model can perform better with fewer forex-specific data points, reducing training time and improving prediction accuracy. This method is especially effective when data availability is an issue, as it allows for the reuse of robust features learned in broader financial contexts.