Skip to main content
# Table 3 CMG algorithm description

From: Air quality forecasting based on cloud model granulation

Algorithm: CMG (TS, winSize, | |
---|---|

Input: Time seriesāāTS, āGranulating window widthāāwinSize, āA number of days to be predictedāā Output: Qualitative predicted feature sequence of cloud model \( {\widehat{E}}_{\mathrm{xi}},{\widehat{E}}_{\mathrm{ni}},{\widehat{H}}_{\mathrm{ei}}\left(i=1,2,\dots, n\right). \) Algorithm steps: A. Granulating the TS by cloud model, the digital feature sequence āa-1. Firstly, the original data series is converted into the granular unit data series according to the window width. āa-2. Second, for each granular unit, the sample mean of each granular unit is calculated \( \overrightarrow{X}=\frac{1}{n}\sum \limits_{i=1}^n{x}_i \),which is the estimated value of expectationā āa-3. Then, it calculates the sample variance \( {S}^2=\frac{1}{n-1}\sum \limits_{i=1}^n{\left({x}_i-\overline{X}\right)}^2 \) and first order sample absolute center moments \( \frac{1}{n}\sum \limits_{i=1}^n\left|{x}_i-\overline{X}\right| \) of each granular; āa-4. Finally, it calculates the entropy \( {E}_n=\sqrt{\frac{\pi }{2}}\times \frac{1}{n}\sum \limits_{i=1}^n\left|{x}_i-{E}_X\right| \) and hyper entropy \( He=\sqrt{S^2-{E_n}^2} \). B. Regression prediction of āb-1. First of all, it uses the grid search method to find the best kernel parameters for āb-2. Then, it established the regression prediction model of āb-3. Finally, it used this model to predict the expectation C. Regression prediction of āc-1. First, this algorithm uses grid search method to find the best kernel parameters for āc-2. Then, it established the regression prediction model of āc-3. Finally, it used this model to predict the entropy D. Regression prediction of ād-1. First, it uses the grid search method to find the best kernel parameters for ād-2. Then, it established the regression prediction model of ād-3. Finally, using the model d-2 to predict |