Mathematical modelling of unemployment dynamics attempts to predict the probability of a job seeker finding a job as a function of time. This is typically achieved by using information in unemployment records. These records are right censored, making survival analysis a suitable approach for parameter estimation. The proposed model uses a deep artificial neural network (ANN) as a non-linear hazard function. Through embedding, high-cardinality categorical features are analysed efficiently. The posterior distribution of the ANN parameters are estimated using a variational Bayes method. The model is evaluated on a time-to-employment data set spanning from 2011 to 2020 provided by the Slovenian public employment service. It is used to determine the employment probability over time for each individual on the record. Similar models could be applied to other questions with multi-dimensional, high-cardinality categorical data including censored records. Such data is often encountered in personal records, for example in medical records.
翻译:失业动态的数学建模试图预测求职者找到工作的概率,这通常通过使用失业记录中的信息来实现。这些记录经过正确审查,使生存分析成为参数估计的合适方法。拟议模型使用深人工神经网络作为非线性危险功能。通过嵌入,对高心性绝对特征进行了高效分析。使用变式贝耶斯方法估算了ANN参数的后端分布。该模型在斯洛文尼亚公共就业服务机构提供的2011年至2020年的工时数据集中进行了评估。该模型用于确定每个记录上个人在时间上的就业概率。类似的模型可用于涉及多维、高心性绝对数据的其他问题,包括受审查的记录。这些数据经常出现在个人记录中,例如医疗记录中。