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 forecasting process. A constantly increasing number of time series has to be forecasted, preferably with low latency AND high accuracy. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In addition, often used forecasting approaches like ARIMA need complete historical data to train forecast models 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 can be calculated quickly. However, this approach is limited by a rigid training data selection and existing forecasting 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 allowing fine grained adaptations to the analyzed data. In this way, we achieve an increased forecast accuracy and thus a wider applicability.

Mit der Verwendung von Sensordaten aus mehreren Quellen entsteht oft die Notwendigkeit einer Synchronisierung der entstandenen Messreihen. Ein Standardverfahren dazu ist die Kreuzkorrelation, die jedoch übereinstimmende Zeitstempel voraussetzt und empfindlich gegenüber Ausreißern reagiert. In diesem Paper wird daher ein alternativer Algorithmus für die Synchronisierung von Ereignis-basierten Zeitreihendaten vorgestellt. / With the use of sensor data from multiple sources, the need for synchronization of the resulting measurement series often arises. A standard method for this is cross-correlation, but this requires matching timestamps and is sensitive to outliers. This paper therefore presents an alternative algorithm for the synchronization of event-based time series data.