Summary form only given. Scientific computing clusters, enterprise data centers and grid and utility environments utilize the majority of the world's computing resources. Most of these resources are lightly utilized and offer a vast potential for resource sharing, an economically attractive and increasingly indispensable management option. A prerequisite for automating resource consolidation is modeling and prediction of demand characteristics. We present an approach for long-term demand characteristics prediction based on mining periodicities in historical demand data. In addition to characterizing the regularity of the past demand behavior (and so providing a measure of predictability) we propose a method for predicting probabilistic profiles which describe likely future behavior. The presented algorithms are change-adaptive in the sense that they automatically adjust to new regularities in demand patterns. A case study using data from an enterprise data center evaluates the effectiveness of the technique.