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 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)
Efficient forecasting for hierarchical time series
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Forecasting is an important data analysis technique and serves as the basis for business planning in many application areas such as energy, sales and traffic management. The currently employed statistical models already provide very accurate predictions, but the forecasting calculation process is very time consuming. This is especially true since many application domains deal with hierarchically organized data. Forecasting in these environments is especially challenging due to ensuring forecasting consistency between hierarchy levels, which leads to an increased data processing and communication effort. For this purpose, we introduce our novel hierarchical forecasting approach, where we propose to push forecast models to the entities on the lowest hierarch level and reuse these models to efficiently create forecast models on higher hierarchical levels. With that we avoid the time-consuming parameter estimation process and allow an almost instant calculation of forecasts.