Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting.
Liyuan JiangZhifu TaoJiaming ZhuJunting ZhangHuayou ChenPublished in: Appl. Intell. (2023)
Keyphrases
- forecasting accuracy
- interval valued
- bp neural network
- financial time series
- texture analysis
- box jenkins
- support vector regression
- chaotic time series
- fuzzy sets
- real valued
- prediction model
- group decision making
- partially ordered
- support vector machine
- empirical mode decomposition
- short term prediction
- support vector machine svm
- neural network
- representation scheme
- support vector
- non stationary
- local binary pattern
- co occurrence
- multi objective
- bidimensional empirical mode decomposition
- interval valued fuzzy
- aggregation operators
- knn
- multiresolution
- evolutionary algorithm
- feature vectors
- computer vision