In streaming data, distributional shifts can appear both in the univariate dimensionsand in the joint distributions with the labels. However, in many real-time scenarios,labels are often either missing or delayed; Unsupervised drift detection methodsare desired in those applications.We design slidSHAPs, a novel representation method for unlabelled data streams.Commonly known in machine learning models, Shapley values offer a way toexploit correlation dependencies among random variables; We develop an unsuper-vised sliding Shapley value series for categorical time series representing the datastream in a newly defined latent space and track the feature correlation changes.Transforming the original time series to the slidSHAPs allows us to track howdistributional shifts affect the correlations among the input variables; the approachis independent of any kind of labeling. We show how abrupt distributional shiftsin the input variables are transformed into smoother changes in the slidSHAPs;Moreover, slidSHAP allows for intuitive visualization of the shifts when they arenot observable in the original data.