  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.

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