On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Optimal combination forecasts for hierarchical time series
Computational Statistics & Data Analysis
Data management in the MIRABEL smart grid system
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Leveraging gamification in demand dispatch systems
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Sample-based forecasting exploiting hierarchical time series
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Forecasting in hierarchical environments
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Forecasting the data cube: A model configuration advisor for multi-dimensional data sets
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Forecasting is used as the basis for business planning in many application areas such as energy, sales and traffic management. Time series data used in these areas is often hierarchically organized and thus, aggregated along the hierarchy levels based on their dimensional features. Calculating forecasts in these environments is very time consuming, due to ensuring forecasting consistency between hierarchy levels. To increase the forecasting efficiency for hierarchically organized time series, we introduce a novel forecasting approach that takes advantage of the hierarchical organization. There, we reuse the forecast models maintained on the lowest level of the hierarchy to almost instantly create already estimated forecast models on higher hierarchical levels. In addition, we define a hierarchical communication framework, increasing the communication flexibility and efficiency. Our experiments show significant runtime improvements for creating a forecast model at higher hierarchical levels, while still providing a very high accuracy.