#CurrencyPairPrediction High-Frequency Data and Supervised Learning Challenges High-frequency data (HFD), often encountered in finance and IoT applications, refers to data collected at very short intervals—sometimes milliseconds or microseconds apart. While this provides detailed insights, it also introduces several challenges for supervised learning models: 1. Volume and Velocity: The massive volume and speed of data require high computational resources and efficient algorithms. 2. Noise and Sparsity: HFD is often noisy, with irregular patterns and outliers, making it harder for models to learn meaningful patterns. 3. Labeling Difficulty: Obtaining accurate, timely labels at high frequency is challenging, leading to weak supervision or delayed feedback. 4. Overfitting Risk: The dense, redundant nature of HFD can cause models to overfit, especially without proper regularization. 5. Concept Drift: Rapid changes in underlying data distributions over time necessitate frequent model retraining or online learning strategies. These factors make supervised learning on HFD complex, demanding robust preprocessing, model tuning, and adaptive learning techniques.