Name
Title | ||
|---|---|---|
Setting forecasting model parameters using unconstrained direct search methods: An empirical evaluation. |
Abstract | ||
|---|---|---|
•We address the parameterization of exponential smoothing forecasting models.•We empirically test derivative-free methods for models’ parameterization.•We adapt the exponential smoothing model to handle box-constraints.•Derivative-free methods almost equaled the performance of a gradient-based method.•Derivative-free methods resulted faster than the gradient-based method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2013.03.044 | Expert Systems with Applications |
Keywords | Field | DocType |
Exponential smoothing forecasting,Time series forecasting,Model parameterization,Direct search methods | Exponential smoothing,Time series,Mathematical optimization,Parametrization,Computer science,Direct search,Robustness (computer science),Compact space,Artificial intelligence,Forecast error,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
40 | 13 | 0957-4174 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Pinto | 1 | 17 | 7.57 |
| Paolo Gaiardelli | 2 | 36 | 10.78 |