A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization.
Cyuan-Heng LuoHsuan YangLi-Pang HuangSachit MahajanLing-Jyh ChenPublished in: TAAI (2018)
Keyphrases
- data analysis
- kernel ridge regression
- multi variate
- forecasting accuracy
- exponential smoothing
- support vector regression
- box jenkins
- reproducing kernel hilbert space
- regression model
- data collection
- generalized regression neural network
- model selection
- data processing
- parameter selection
- linear regression
- chaotic time series
- arima model
- regularization parameter
- phase space reconstruction
- demand forecasting
- short term
- dynamic time warping
- gradient boosting
- autoregressive conditional heteroscedasticity
- forecasting model
- machine learning
- rough sets
- cross validation
- regression problems
- multivariate time series
- regression analysis
- regression method
- moving average
- business intelligence
- non stationary
- aggregating algorithm
- artificial neural networks
- kernel methods
- hybrid model
- gaussian processes
- support vector
- regression algorithm
- mackey glass
- neural network
- cluster analysis
- empirical risk minimization
- regression function
- regularization term
- partial least squares