Name
Abstract | ||
|---|---|---|
Using historical data to forecast future trends in time series is a key application of data mining. This paper deals with the problem of time series forecasting using the non-parametric Gaussian process model. The time series forecasting is accomplished by using multiple Gaussian process models of each step ahead predictor in accordance with the direct approach. The separable least-squares approach is applied to train these Gaussian process models. Hyperparameters of the covariance function are coded into binary bit strings and candidate weighting parameters of the mean function corresponding to each candidate of hyperparameters are estimated by the linear least-squares method. The genetic algorithm is utilized to determine these unknown hyperparameters by minimizing the negative log marginal likelihood of the training data. Simulation results are shown to illustrate the proposed forecasting method and compared with the iterated prediction method. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CIDM.2007.368931 | 2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, VOLS 1 AND 2 |
Keywords | Field | DocType |
least square,gaussian processes,marginal likelihood,covariance function,time series,gaussian process,time series forecasting,genetic algorithm,data mining | Time series,Weighting,Covariance function,Hyperparameter,Computer science,Marginal likelihood,Artificial intelligence,Gaussian process,Iterated function,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.49 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiro Hachino | 1 | 8 | 2.37 |
| Visakan Kadirkamanathan | 2 | 431 | 62.00 |