Name
Abstract | ||
|---|---|---|
•Temporal hierarchies can be used for any time series to improve forecasts.•Temporal reconciliation results in accurate and robust forecasts.•New estimators that account for auto- and cross-correlation in reconciling forecasts.•Information sharing between aggregation levels significantly improves forecasts.•Out-of-sample improvements in accuracy of 68% when forecasting load. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ejor.2019.07.061 | European Journal of Operational Research |
Keywords | Field | DocType |
Forecasting,Forecast combination,Temporal aggregation,Autocorrelation,Reconciliation | Econometrics,Autocovariance,Inverse,Mathematical optimization,Matrix (mathematics),Load forecasting,Hierarchy,Information sharing,Mathematics,Autocorrelation,Estimator | Journal |
Volume | Issue | ISSN |
280 | 3 | 0377-2217 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Nystrup | 1 | 3 | 1.77 |
| Erik Lindström | 2 | 23 | 4.04 |
| Pierre Pinson | 3 | 73 | 14.82 |
| Henrik Madsen | 4 | 119 | 25.49 |