Hierarchical Time Series

Hierarchical time series forecasting focuses on predicting multiple interconnected time series organized in a hierarchical structure, ensuring consistency across different aggregation levels (e.g., sales data aggregated by region, product, and time). Current research emphasizes developing sophisticated models, including graph neural networks and hybrid approaches combining tree-based methods with neural networks, to improve forecast accuracy and coherence while handling the complexities of varying forecastability across hierarchical levels. These advancements are crucial for diverse applications, such as supply chain optimization, energy forecasting, and financial modeling, where accurate and consistent predictions across multiple scales are essential for effective decision-making. The field is also actively exploring robust evaluation frameworks and efficient algorithms to handle increasingly large and complex datasets.