动态因子和双PCA模型用于部分观测的生存曲线：短期租赁市场需求预测
Dynamic factor and double PCA models for partially observed survival curves: Forecasting demand in short-term rental markets
- Norwegian Computing Center（挪威计算中心）
- Department of Mathematics, University of Oslo（奥斯陆大学数学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出动态因子和双PCA模型，用于部分观测生存曲线的预测，并在短期租赁市场入住率数据上验证，优于Holt方法，支持动态定价决策。
AI中文摘要：
本文开发了针对随时间观测的、从异质群体混合中抽样得到的群体水平生存曲线的预测模型。我们考虑一个离散时间设定，其中每条曲线仅被部分观测，并且需要对剩余轨迹进行预测以支持下游决策。我们的方法将跨群体异质性重新表述为一个多元抽样模型。我们提出了两种针对部分观测曲线的预测模型：一种全因子分析模型，将一般因子表示扩展以纳入部分观测的生存曲线，以及一种双PCA模型。该方法的动机源于短期租赁市场的需求预测，其中市场层面的入住率路径可视为预订视野内的生存曲线，而对未来入住率的预测则输入动态定价算法。我们将这些模型应用于新发布的Wheelhouse数据集，该数据集包含2017年至2022年500个市场的市场入住率曲线时间序列。模型性能使用积分二次距离进行评估，并将所提出的基于PCA的方法与Holt线性趋势模型在多个预测视野内进行比较。结果表明，所提出的模型对剩余生存轨迹产生了准确且稳定的预测，并且通常优于Holt方法，尤其是在更长的预测视野下。
英文摘要：
This paper develops prediction models for population-level survival curves observed over time and sampled from a heterogeneous mix of populations. We consider a discrete-time setting where each curve is only partially observed and forecasts of the remaining trajectory are needed for downstream decision making. Our approach recasts cross-population heterogeneity into a multivariate sampling model. We propose two forecasting models for partially observed curves: a full factor analysis model that extends a general factor representation to incorporate the partially observed survival curve, and a double PCA model. The methodology is motivated by demand forecasting in short-term rental markets, where market-level occupancy paths can be viewed as survival curves over the booking horizon and where forecasts of future occupancy feed into dynamic pricing algorithms. We apply the models to the newly released Wheelhouse dataset, which contains time series of market occupancy curves for 500 markets from 2017 to 2022. Model performance is assessed using the integrated quadratic distance, and we compare the proposed PCA-based methods to Holt's linear trend model across multiple forecast horizons. The results show that the proposed models yield accurate and stable forecasts of the remaining survival trajectory and generally outperform Holt's method, particularly at longer horizons.
