用图谱滤波增强时序网络，提升区域数据预测精度
Echo State Networks for Spatio-Temporal Area-Level Data
- 在ESN输入层加入近似图谱滤波，建模区域空间关系
- 在欧洲旅游入住率数据上实现更优预测表现
- 适合需要空间依赖建模的政策与规划场景
区域级时空数据在官方统计中至关重要，对政策制定和区域规划具有重要价值。准确建模与预测此类数据有助于决策者制定前瞻性策略。回声状态网络（ESNs）能高效捕捉非线性时间动态并生成预测，但缺乏直接处理区域数据固有邻域结构的能力。忽略空间关联会显著降低预测准确性与实用性。本文在ESN输入阶段引入近似图谱滤波，有效整合空间结构信息，在保持训练效率的同时提升预测性能。实验基于欧盟统计局的旅游入住率数据集，验证了该方法的有效性，可为政策与规划提供更可靠的决策支持。
原文摘要 · Abstract (English)
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.
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