Name
Title | ||
|---|---|---|
Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. |
Abstract | ||
|---|---|---|
•A systematic literature review of the last decade.•One of the most extensive, impartial and comprehensible evaluations ever done in the time series prediction field.•Recommendation of the most suitable predictors based on a critical analysis.•A guideline for models selection, parameters setting and validation of results.•A new online data repository for time series forecasting. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.01.076 | Information Sciences |
Keywords | Field | DocType |
Univariate analysis,Automatic parameter tuning,Multi-step-ahead prediction,Time series forecasting,Data mining | Time series,Nonlinear system,Empirical evidence,Systematic review,Support vector machine,Temporal database,Parametric statistics,Artificial intelligence,Mathematics,Replicate,Machine learning | Journal |
Volume | ISSN | Citations |
484 | 0020-0255 | 1 |
PageRank | References | Authors |
0.41 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Rafael Sabino Parmezan | 1 | 11 | 2.30 |
| Vinícius M. A. de Souza | 2 | 33 | 6.14 |
| Gustavo E. Batista | 3 | 1928 | 92.83 |