Multivariate reconciliation for hierarchical time series
层级时间序列的多变量预测协调AI 解读 ↓
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层级时间序列的多变量预测协调
- 研究问题
- 当多个变量分别具有层级加总约束且彼此相关时，如何联合调整基础预测，使预测满足约束并利用变量之间的联系？
- 方法与思路
- 作者提出多变量预测协调方法，在满足层级一致性的同时纳入变量间关系，并以不同层级情景的模拟及巴西就业数据评估。
- 材料陈述的贡献
- 摘要称该方法克服逐变量协调忽略跨变量相关性的局限，在所考察的模拟与就业应用中，比比较方法获得更准确的预测。
- 阅读关注点
- 应核对联合协调的目标函数、相关结构估计、加总约束，以及基础预测模型和样本外评估设计是否支持比较结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要未给出误差指标、改善幅度及适用条件，不能将所述优势推广到所有层级数据。
原文摘要
Some time series can be hierarchically organized into levels based on certain characteristics, such as geography or other attributes of interest. These series are referred to as hierarchical time series. Typically, forecasts are generated at all levels to ensure coherence, meaning that the forecasts should satisfy the same aggregation constraints as the observed data. Various approaches have been proposed to guarantee this coherence by using a set of base forecasts. The process through which these forecasts are adjusted to become coherent is known as forecast reconciliation. Similar to the univariate case, multivariate time series can also be structured hierarchically. However, all existing approaches are limited to a single variable. As a result, ensuring coherent forecasts requires reconciling each variable separately. However, this process does not account for correlations among multiple variables. To address this limitation, this paper proposes a multivariate reconciliation methodology that ensures coherent forecasts and incorporates relationships among variables. The proposed methodology was tested through numerical simulations, considering distinct scenarios within the series hierarchy and across multiple variables. Additionally, some base forecasting models were evaluated. The methodology was also applied to real employment data of admissions and dismissals in Brazil. The results demonstrated that multivariate reconciliation yielded more accurate outcomes than the other methods considered, both in simulated data and in practical applications.