#AITradingAffectsForex AI for Automated Data Cleaning and Preprocessing in Forex Automated data cleaning and preprocessing are crucial in Forex trading, where massive amounts of financial data must be processed quickly and accurately. AI-driven techniques enhance this process by improving efficiency, reducing errors, and preparing high-quality data for analysis. Key AI Techniques for Data Cleaning and Preprocessing in Forex 1. Missing Data Handling AI models use imputation techniques (e.g., K-Nearest Neighbors, regression, deep learning) to fill in missing Forex data. Machine learning can predict missing values based on historical trends. 2. Anomaly Detection and Error Correction AI detects outliers caused by sudden market fluctuations, incorrect entries, or sensor errors. Algorithms like Isolation Forest, Autoencoders, and statistical models identify and correct inconsistencies. 3. Data Normalization and Scaling Forex data from different sources can have varying scales; AI applies Min-Max scaling, Z-score normalization, and log transformations. Ensures different currency pairs and indicators are on a comparable scale for accurate modeling. 4. Feature Engineering and Selection AI automates the selection of the most relevant Forex indicators (e.g., moving averages, RSI, MACD) to reduce noise. Deep learning can generate new features that improve model performance. 5. Data Deduplication and Integration AI removes duplicate records from multiple Forex data sources to avoid redundancy. Integrates real-time