Name
Abstract | ||
|---|---|---|
Graphical abstractDisplay Omitted HighlightsProposing a local modeling strategy, consisting of local context finding and lags selection, for time series prediction.Developing algorithms to extract training patterns from historical data for constructing forecasting models.Developing procedures to construct neuro-fuzzy based forecasting models.Demonstrating by experiments on real-world datasets the effectiveness of the proposed approach. Time series forecasting concerns the prediction of future values based on the observations previously taken at equally spaced time points. Statistical methods have been extensively applied in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose an approach based on neuro-fuzzy modeling for time series prediction. Given a predicting sequence, the local context of the sequence is located in the series of the observed data. Proper lags of relevant variables are selected and training patterns are extracted. Based on the extracted training patterns, a set of TSK fuzzy rules are constructed and the parameters involved in the rules are refined by a hybrid learning algorithm. The refined fuzzy rules are then used for prediction. Our approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate prediction. It also works for one-step as well as multi-step prediction. Several experiments are conducted to demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.asoc.2015.03.059 | Applied Soft Computing |
Keywords | DocType | Volume |
Time series prediction,Neuro-fuzzy modeling,TSK fuzzy rules,Learning algorithm,Training patterns | Journal | 32 |
Issue | ISSN | Citations |
C | 1568-4946 | 13 |
PageRank | References | Authors |
0.82 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung-Wen Peng | 1 | 13 | 1.15 |
| Shen-Fu Wu | 2 | 13 | 0.82 |
| Chia-Ching Wei | 3 | 13 | 0.82 |
| Shie-Jue Lee | 4 | 48 | 5.11 |