Name
Abstract | ||
|---|---|---|
Many forecasting models based on the concepts of fuzzy time series have been proposed in the past decades. These models have been applied to predict enrollments, temperature, crop production and stock index, etc. In this paper, we present a simple heuristic time-invariant fuzzy time series forecasting model, which uses prediction accuracy of model observations to train the trend predictor in the training phase, and uses these trend predictor to generate forecasting values in the testing phase. This model can capture the trends of the time series more accurately and hence improve the forecasting results. The proposed method is applied for forecasting university enrollment of Alabama and the Taiwan Futures Exchange (TAIFEX). It is shown that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy time series forecasting models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2010.08.059 | Expert Syst. Appl. |
Keywords | Field | DocType |
time series,trend predictor,fuzzy time series forecasting,heuristic model,series forecasting model,fuzzy time series,taifex,model observation,heuristic time-invariant model,heuristic time-invariant fuzzy time,forecasting result,enrollment,forecasting,forecasting model,time series forecasting,indexation | Technology forecasting,Time series,Data mining,LTI system theory,Heuristic,Futures contract,Stock market index,Computer science,Fuzzy logic,Artificial intelligence,Probabilistic forecasting,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
12 | 0.49 | 17 |
Authors | ||
5 | ||