Time Series Decomposition

Time series decomposition aims to separate a time series into constituent components like trend, seasonality, and residuals, facilitating easier analysis and forecasting. Current research emphasizes integrating decomposition with advanced machine learning models, such as neural ordinary differential equations, graph convolutional networks, and novel recurrent neural architectures designed to capture inter-component relationships. This approach improves forecasting accuracy and interpretability across diverse applications, from environmental monitoring (e.g., chlorophyll prediction) to financial forecasting and anomaly detection, by allowing for tailored modeling of each component's unique characteristics. The resulting enhanced accuracy and explainability are driving significant advancements in various fields.