The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy character of these domains in combination with the logistic problems of collecting data from a large number of data sources imposes new requirements on the forecast process. A constantly increasing number of time series has to be forecast over several periods in order to enable long-term planning with high accuracy and short execution time. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecast techniques like ARIMA require complete historical data and fail if time series are intermittent. A method that addresses all these new requirements is the cross-sectional forecasting approach. It utilizes available data from many time series of the same domain in one single model; thus, missing values can be compensated and accurate forecast results are calculated quickly. However, this approach is limited by a rigid data selection and existing forecast methods show that adaptability of the model to the data increases the forecast accuracy. Therefore in this paper, we present CSAR, a model that extends the cross-sectional paradigm by adding more flexibility and allows fine-grained adaptations toward the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability.