Forecasting the data cube: A model configuration advisor for multi-dimensional data sets

Fischer, Ulrike; Schildt, C.; Hartmann, Claudio; Lehner, Wolfgang

doi:10.1109/icde.2013.6544880

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…Here, the time series are typically aggregated along the hierarchy based on dimensional attributes such as location or product [96,98,132]. However, besides the context information in the sense of external influences, there is also a further class of additional information provided for some time series.…”

Section: Exploiting Hierarchical Time Series Structuresmentioning

confidence: 99%

Energy Time Series Forecasting

Dannecker¹

2015

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Section: Exploiting Hierarchical Time Series Structuresmentioning

confidence: 99%

Energy Time Series Forecasting

Dannecker¹

2015

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“…The general settings for this experiment are the same as for the DSHWT model. However, since the EGRV model contains both exogenous and endogenous coefficients, we also compared the accuracy when using our weighted aggregation described in Equation 8.…”

Section: Results Egrv Parameter Adjustmentmentioning

confidence: 99%

“…Additionally, many application domains exhibit a hierarchical data organization, with time series and forecast models on multiple levels. Here, the time series are aggregated along the hierarchy based on dimensional attributes such as location [14,7,8]. Forecasting in these environments is especially complex since it is necessary to involve data and entities across hierarchical levels and to ensure forecasting consistency among them.…”

Section: Introductionmentioning

confidence: 99%

Efficient forecasting for hierarchical time series

Dannecker

Lorenz

Rösch

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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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.

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“…Fischer et al [4,5] published an sampling approach using only a sample of base forecast models for forecasting in hierarchies. Thus, forecasts on a specific hierarchy level may be based on a subset of optimized models on other levels.…”

Section: Related Workmentioning

confidence: 99%

Forecasting in hierarchical environments

Lorenz

Dannecker

Rösch

et al. 2013

Proceedings of the 25th International Conference on Scientific and Statistical Database Management

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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.

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Forecasting the data cube: A model configuration advisor for multi-dimensional data sets

Cited by 11 publications

References 20 publications

Energy Time Series Forecasting

Energy Time Series Forecasting

Efficient forecasting for hierarchical time series

Forecasting in hierarchical environments

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