Abstract
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.
Funding source: European Regional Development Fund
Award Identifier / Grant number: 100320127
Funding source: Horizon 2020 Framework Programme
Award Identifier / Grant number: 731232
Funding statement: This work is partly funded (1) by the European Regional Development Fund (ERDF) under co-financing by the Free State of Saxony (100320127) and Systema GmbH, and (2) within the European Union’s Horizon 2020 research and innovation program under grant agreement No. 731232.
About the authors
Claudio Hartmann studied Computer Science at the Technische Universität Dresden (Diplom 2013, Promotion 2018). Currently, he is postdoc at the Database Systems Research Group of Prof. Wolfgang Lehner (since 2018). His research focuses on time series forecasting, especially, the prediction of large-scale data sets.
Lars Kegel studied Computer Science at Technische Universität Dresden where he received his diploma in 2015. His research focuses on feature-based time series analytics, i. e., on statistical representations for time series data sets and their use in data-mining tasks.
Wolfgang Lehner is full professor and head of the Database Systems Research Group at Technische Universität Dresden. His research interests range from designing data-management infrastructures from a modeling perspective, supporting data-intensive applications and processes in large distributed information systems, adding novel database functionality to relational database engines to support data science application, and exploiting modern hardware capabilities to optimize main-memory database systems.
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