Name
Abstract | ||
|---|---|---|
This paper proposes an accurate hybrid method based on support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to predict the tidal current speed and direction. In the proposed hybrid model, the ARIMA model captures the linear component of the tidal current, and the remained residual components are modeled by SVR. In order to capture the maximum linear components, the ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TGRS.2016.2596320 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Mathematical model,Predictive models,Training,Support vector machines,Data models,Time series analysis,Indexes | Data modeling,Time series,Artificial intelligence,Autocorrelation,Computer vision,Residual,Akaike information criterion,Support vector machine,Algorithm,Autoregressive integrated moving average,Partial autocorrelation function,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
55 | 1 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdollah Kavousi-Fard | 1 | 268 | 31.99 |