Non Stationary Data

Non-stationary data, characterized by evolving statistical properties over time, poses significant challenges for machine learning models. Current research focuses on developing methods to effectively model and predict from such data, employing techniques like instance normalization, self-organizing maps, Kalman filters, and siamese neural networks, often within online learning frameworks. These advancements are crucial for improving the accuracy and robustness of models in diverse applications, including time series forecasting, continual learning, and anomaly detection in domains with dynamic characteristics like sensor data analysis and neuroscience. The development of robust algorithms for handling non-stationarity is essential for reliable predictions and insights from increasingly prevalent real-world data streams.