Details

Autor(en) / Beteiligte

• Dannecker, Lars
• Lorenz, Robert
• Rösch, Philipp
• Lehner, Wolfgang
• Hackenbroich, Gregor

Titel

Efficient forecasting for hierarchical time series

Ist Teil von

• Proceedings of the 22nd ACM international conference on information & knowledge management, 2013, p.2399-2404

Ort / Verlag

ACM

Erscheinungsjahr

2013

Link zum Volltext

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• 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.