Seasonal Trend Decomposition

Seasonal trend decomposition aims to separate time series data into constituent components like trend, seasonality, and residuals, facilitating improved analysis and forecasting. Current research emphasizes developing efficient algorithms, such as those based on transformers and other neural networks, that can handle large datasets and real-time processing, often incorporating novel sampling techniques to avoid data leakage. These advancements improve accuracy and efficiency in diverse applications, including anomaly detection, hydrological forecasting, and retail sales prediction, ultimately leading to better decision-making across various fields.