http://dl.gi.de/handle/20.500.12116/36562 2026-07-21T13:39:38Z 2026-07-21T13:39:38Z Hartmann, Claudio Kegel, Lars Lehner, Wolfgang http://dl.gi.de/handle/20.500.12116/36568 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Feature-aware forecasting of large-scale time series data sets Hartmann, Claudio; Kegel, Lars; Lehner, Wolfgang The Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and missing values. This work presents CSAR, a technique forecasting a set of time series with only one model, and a feature-aware partitioning applying CSAR on subsets of similar time series. These techniques provide accurate forecasts a hundred times faster than traditional techniques, preparing forecasting for the arising challenges of the IoT era.

2020-01-01T00:00:00Z Meyer, Ulrich Abedjan, Ziawasch http://dl.gi.de/handle/20.500.12116/36564 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Algorithms for Big Data Meyer, Ulrich; Abedjan, Ziawasch Article Algorithms for Big Data was published on June 1, 2020 in the journal it - Information Technology (volume 62, issue 3-4).

2020-01-01T00:00:00Z Frontmatter http://dl.gi.de/handle/20.500.12116/36563 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Frontmatter Frontmatter Article Frontmatter was published on June 1, 2020 in the journal it - Information Technology (volume 62, issue 3-4).

2020-01-01T00:00:00Z Giesen, Joachim Laue, Sören Mitterreiter, Matthias http://dl.gi.de/handle/20.500.12116/36569 2021-06-21T12:23:08Z 2020-01-01T00:00:00Z

Optimization frameworks for machine learning: Examples and case study Giesen, Joachim; Laue, Sören; Mitterreiter, Matthias Mathematical optimization is at the algorithmic core of machine learning. Almost any known algorithm for solving mathematical optimization problems has been applied in machine learning and the machine learning community itself is actively designing and implementing new algorithms for specific problems. These implementations have to be made available to machine learning practitioners which is mostly accomplished by distributing them as standalone software. Successful well-engineered implementations are collected in machine learning toolboxes that provide a more uniform access to the different solvers. A disadvantage of the toolbox approach is a lack of flexibility as toolboxes only provide access to a fixed set of machine learning models that cannot be modified. This can be a problem for the typical machine learning workflow that iterates the process of modeling, solving and validating. If a model does not perform well on validation data, it needs to be modified. In most cases these modifications require a new solver for the entailed optimization problems. Optimization frameworks that combine a modeling language for specifying optimization problems with a solver are better suited to the iterative workflow since they allow to address large problem classes. Here, we provide examples of the use of optimization frameworks in machine learning. We also illustrate the use of one such framework in a case study that follows the typical machine learning workflow.

2020-01-01T00:00:00Z