Non Stationary Time Series

Non-stationary time series analysis focuses on modeling and forecasting data where statistical properties change over time, a common characteristic in many real-world datasets. Current research emphasizes developing robust algorithms and model architectures, such as transformers and recurrent neural networks, often incorporating techniques like frequency domain normalization, dynamic pattern extraction, and causal inference to improve forecasting accuracy and anomaly detection in non-stationary data. These advancements are crucial for various applications, including financial forecasting, climate modeling, and healthcare, where accurate predictions from inherently dynamic data are essential. The field is actively exploring methods to handle the challenges posed by evolving data distributions and spurious correlations, leading to more reliable and interpretable results.