Financial markets provide vast numbers of signals about the performance of companies, banks, assets and economies. These signals can be used by risk managers and regulators to better understand economic dependencies, correlations and phase transitions. In this paper, we present a methodology for mapping multiple dimensions of time series data into two-dimensional visual layouts by applying methods from statistics and network theory. The methodology involves identifying important correlations between the time series as well as monitoring individual series to determine which ones have extreme return values compared with their past performance. Analysis is presented visually to give quick insight into a complex system moving in time; for example, systemically important assets are easily recognizable as those that are central in the minimum spanning tree structure of the correlation matrix, and systemic events are visible as large numbers of assets having extreme values. We present historical scenarios to illustrate the methodology.