  Many time series in smart energy systems exhibit two different timescales. On
the one hand there are patterns linked to daily human activities. On the other
hand, there are relatively slow trends linked to seasonal variations. In this
paper we interpret these time series as matrices, to be visualized as images.
This approach has two advantages: First of all, interpreting such time series
as images enables one to visually integrate across the image and makes it
therefore easier to spot subtle or faint features. Second, the matrix
interpretation also grants elucidation of the underlying structure using
well-established matrix decomposition methods. We will illustrate both these
aspects for data obtained from the German day-ahead market.
