Name
Title | ||
|---|---|---|
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach. |
Abstract | ||
|---|---|---|
•Building a model on a set of related time series can improve the forecast accuracy.•Performance of the global models can degenerate if built on disparate time series.•A subgrouping strategy then augments the accuracies of the baseline global models. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2019.112896 | Expert Systems with Applications |
Keywords | Field | DocType |
Big data forecasting,RNN,LSTM,Time series clustering,Neural networks | Time series,Recurrent neural network,Time series database,Artificial intelligence,Cluster analysis,Univariate,Big data,Benchmarking,Database,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
140 | 0957-4174 | 5 |
PageRank | References | Authors |
0.41 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kasun Bandara | 1 | 10 | 2.35 |
| Christoph Bergmeir | 2 | 152 | 14.04 |
| Slawek Smyl | 3 | 6 | 1.47 |