In this paper we apply a time series based Vector Auto Regressive (VAR) approach to the problem of predicting unemployment insurance claims in different census regions of the United States. Unemployment insurance claims data, reported weekly, are a leading indicator of the US unemployment rate. Gathering weekly unemployment claims and aggregating by region, we model correlation between the different census regions. Additionally, we explore the use of external variables such as Bing search query volumes and URL site clicks related to unemployment claims. To prevent any spurious predictors from appearing in the model we use sparse model based regularization. Preliminary results indicate that our approach is promising and in ongoing work we are extending the approach to a larger set of predictors and a longer data range.

翻译：在本文中,我们采用基于时间序列的矢量自动递减(VAR)方法来预测美国不同普查区域的失业保险索赔要求。每周报告的失业保险索赔数据是美国失业率的一个主要指标。每周收集失业索赔要求并按区域汇总,我们以不同普查区域为模型。此外,我们探索使用与失业索赔有关的外部变量,如宾搜索查询量和URL网站点击。为了防止任何虚假预测者出现在我们使用的基于稀少模型的模型中。初步结果显示,我们的方法很有希望,在目前的工作中,我们正在将这一方法扩大到更多的预测数据和更长的数据范围。