Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecasting of these datasets can be extremely useful for policymakers to develop informed strategies for future planning. Echo State Networks (ESNs) are efficient methods for capturing nonlinear temporal dynamics and generating forecasts. However, ESNs lack a direct mechanism to account for the neighborhood structure inherent in area-level data. Ignoring these spatial relationships can significantly compromise the accuracy and utility of forecasts. In this paper, we incorporate approximate graph spectral filters at the input stage of the ESN, thereby improving forecast accuracy while preserving the model's computational efficiency during training. We demonstrate the effectiveness of our approach using Eurostat's tourism occupancy dataset and show how it can support more informed decision-making in policy and planning contexts.

翻译：时空区域级数据集在官方统计中扮演着关键角色，为政策制定和区域规划提供了宝贵的洞察。对这些数据集进行精确建模与预测，能够极大地帮助决策者制定基于充分信息的未来规划策略。回声状态网络（ESNs）是捕捉非线性时间动态并生成预测的有效方法。然而，ESNs缺乏直接处理区域级数据中固有邻域结构的机制。忽略这些空间关系会显著损害预测的准确性与实用性。本文在ESN的输入阶段引入近似图谱滤波器，从而在保持模型训练阶段计算效率的同时，提升了预测精度。我们利用欧盟统计局（Eurostat）的旅游入住率数据集验证了所提方法的有效性，并展示了该方法如何在政策与规划情境中支持更明智的决策制定。