For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the variety of approaches, there is no comparative and cross-domain assessment of generation methods and their expressiveness. We present a similarity measure that analyzes generation methods regarding general time series features. By this means, users can compare generation methods and validate whether a generated dataset is considered similar to a given dataset. Moreover, we propose a feature-based generation method that evolves cross-domain time series datasets. This method outperforms other generation methods regarding the feature-based similarity.

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.

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions are either costly or require continuous maintenance. In this paper we propose an approach for approximate OLAP querying of time series that offers constant latency and is maintenance-free. To achieve this, we identify similarities between aggregation cuboids and propose algorithms that eliminate the redundancy these similarities present. In doing so, we can achieve compression rates of up to 80% while maintaining low average errors in the query results.

The role of precise forecasts in the energy domain has changed dramatically. New supply forecasting methods are developed to better address this challenge, but meaningful benchmarks are rare and time-intensive. We propose the ECAST online platform in order to solve that problem. The system's capability is demonstrated on a real-world use case by comparing the performance of different prediction tools.

Forecasting 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 from many heterogeneous data sources in more and more applications domains. The highly dynamic and often fluctuating character of these domains in combination with the logistic problems of collecting such data from a variety of sources, imposes new challenges to forecasting. Traditional approaches heavily rely on extensive and complete historical data to build time series models and are thus no longer applicable if time series are short or, even more important, intermittent. In addition, large numbers of time series have to be forecasted on different aggregation levels with preferably low latency, while forecast accuracy should remain high. This is almost impossible, when keeping the traditional focus on creating one forecast model for each individual time series. In this paper we tackle these challenges by presenting a novel forecasting approach called cross-sectional forecasting. This method is especially designed for Big Data sets with a multitude of time series. Our approach breaks with existing concepts by creating only one model for a whole set of time series and requiring only a fraction of the available data to provide accurate forecasts. By utilizing available data from all time series of a data set, missing values can be compensated and accurate forecasting results can be calculated quickly on arbitrary aggregation levels.

The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.