# Forecasting the Natural Gas Supply and Consumption in China Using a Novel Grey Wavelet Support Vector Regressor

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## Abstract

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## 1. Introduction

## 2. The Proposed Grey Wavelet Support Vector Regressor

#### 2.1. Grey System Model with Nonlinear Mapping and Its Solution

#### 2.2. The $\epsilon $-Insensitive Loss for the Proposed Model

#### 2.3. The Kernel Representation and the Wavelet Kernel

#### 2.4. Training Algorithm for the GWSVR

#### 2.5. Response Function and the Predicted Values

#### 2.6. The Complete Overal Computational Algorithm

Algorithm 1: Overall computation algorithm of GWSVR |

## 3. Hyperparameter Optimization by the Grey Wolf Optimizer

#### 3.1. Construction of the Hyperparameter Optimization Scheme

#### 3.2. The Grey Wolf Optimizer

Algorithm 2: Algorithm for solving the optimization problem by GWO |

## 4. Applications

#### 4.1. Data Collection and Preprocessing

#### 4.2. Benchmarked Models for Comparisons and Evaluation Metrics

#### 4.3. Forecasting Results and Analysis

#### 4.3.1. Case I: Forecast of the Total Natural Gas Available for Consumption in China

#### 4.3.2. Case II: Forecast of the Total Natural Gas Consumption in China

#### 4.4. Discussion

#### 4.4.1. Comparisons of the GWSVR Model and Linear Grey System Models

#### 4.4.2. Comparisons of the GWSVR Model and Nonlinear Grey System Models

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Metrics | Abbreviation | Expression |
---|---|---|

Mean Absolute Error | MAE | $\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}\left|{y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right|$ |

Mean Absolute Percentage Error | MAPE | $\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}\left|\frac{{y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)}{y\left(k\right)}\right|\times 100\%$ |

Mean Square Error | MSE | $\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}$ |

Root Mean Square Error | RMSE | $\sqrt{\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}$ |

Theil U Statistic 1 | U1 | $\frac{\sqrt{\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}}{\sqrt{\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)\right)}^{2}}+\sqrt{\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}}$ |

Theil U Statistic 2 | U2 | $\frac{\sqrt{\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}}{\sqrt{{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)\right)}^{2}}}$ |

Median Absolute Error | MedAe | $\frac{1}{n}{\displaystyle \sum _{k=1}^{n}}arctan\left(\left|\frac{{y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)}{y\left(k\right)}\right|\right)$ |

Index of Agreement | IA | $1-\frac{{\sum}_{k=1}^{n}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}{{\sum}_{k=1}^{n}{\left(\left|{y}^{\left(0\right)}\left(k\right)-{\overline{y}}^{\left(0\right)}\right|+\left|{\widehat{y}}^{\left(0\right)}\left(k\right)-{\overline{\widehat{y}}}^{\left(0\right)}\right|\right)}^{2}}$ |

Coefficient of Determination | R${}^{2}$ | $1-\frac{{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}^{2}}{{\displaystyle \sum _{k=1}^{n}}{\left({y}^{\left(0\right)}\left(k\right)-{\overline{y}}^{\left(0\right)}\right)}^{2}}$ |

Percent Bias | Pibas | $\frac{{\displaystyle \sum _{k=1}^{n}}\left({y}^{\left(0\right)}\left(k\right)-{\widehat{y}}^{\left(0\right)}\left(k\right)\right)}{{\displaystyle \sum _{k=1}^{n}}{\widehat{y}}^{\left(0\right)}\left(k\right)}$ |

Model | Model Abbreviation | Optimal Hyperparameters | Training Time (s) |
---|---|---|---|

Grey Wavelet Support Vector Regressor | GWSVR | C = 279.8859, $\nu $ = 0.2920 | 0.84743 s |

Nonhomogeneous Grey Bernoulli Model | NGBM | n = 5.8944 | 0.03722 s |

Fractional-order Grey Model | FGM | r = 0.2582 | 0.15140 s |

Fractional-order Nonhomogeneous Grey Model | FNGM | r = 1.3683 | 0.09306 s |

Fractional-order Discrete Grey Model | FDGM | r = 0.2377 | 0.10684 s |

Fractional-order Nonhomogeneous Discrete Grey Model | FNDGM | r = −1.0525 | 0.61533 s |

New Information Priority Grey Model | NIPGM | r = 0.1218 | 0.05067 s |

New Information Priority Nonhomogeneous Grey Model | NIPNGM | r = 0.1956 | 0.05541 s |

New Information Priority Discrete Grey Model | NIPDGM | r = 0.8181 | 0.04859 s |

New Information Priority Nonhomogeneous Discrete Grey Model | NIPNDGM | r = 0.7722 | 0.19020 s |

Nonlinear Grey Bernoulli Model with Fractional-order Accumulation | FNGBM | n = 2.0000, r = −0.0255 | 0.78871 s |

New Information Priority Nonlinear Grey Bernoulli Model | NIPNGBM | n = 2.0000, r = 0.0000 | 0.42991 s |

GWSVR | GM | NGM | DGM | NDGM | NGBM | FGM | FNGM | FDGM | FNDGM | NIPGM | NIPNGM | NIPDGM | NIPNDGM | FNGBM | NIPNGBM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

MAE | 67.67645 | 838.3097 | 248.2112 | 856.624 | 353.4656 | 1931.666 | 681.2515 | 634.3292 | 671.4348 | 375.5022 | 720.9939 | 1633.579 | 573.2332 | 1294.021 | 294.8063 | 213.4934 |

MAPE | 2.671638 | 30.90473 | 9.520339 | 31.5974 | 13.63293 | 69.15081 | 25.57323 | 23.52015 | 25.24756 | 14.80201 | 27.01877 | 60.17945 | 21.68454 | 47.20952 | 12.29786 | 9.316916 |

MSE | 6210.548 | 851,978.1 | 71,475.37 | 887,239.9 | 136,967.9 | 4,851,922 | 530,154.6 | 480,953.7 | 512,394.5 | 146,942.1 | 596,039.9 | 3,214,052 | 368,567.4 | 2,103,858 | 93,267.14 | 57,426.36 |

RMSE | 78.80703 | 923.0266 | 267.3488 | 941.9341 | 370.0917 | 2202.708 | 728.1172 | 693.5083 | 715.8174 | 383.3302 | 772.0362 | 1792.778 | 607.0975 | 1450.468 | 305.3967 | 239.638 |

U1 | 0.014978 | 0.14954 | 0.048349 | 0.152132 | 0.065671 | 0.628413 | 0.121569 | 0.116579 | 0.119747 | 0.067848 | 0.127981 | 0.254925 | 0.103404 | 0.217042 | 0.054986 | 0.043823 |

U2 | 0.029889 | 0.350072 | 0.101396 | 0.357243 | 0.140363 | 0.835411 | 0.27615 | 0.263024 | 0.271485 | 0.145384 | 0.292807 | 0.679939 | 0.230251 | 0.550112 | 0.115826 | 0.090886 |

MedAe | 60.64835 | 726.0962 | 249.7691 | 743.6731 | 346.3844 | 2072.833 | 599.266 | 545.3121 | 591.2506 | 381.9754 | 636.0132 | 1447.303 | 502.216 | 1114.763 | 280.7869 | 200.0508 |

IA | 0.993118 | 0.676358 | 0.939349 | 0.66958 | 0.893418 | 0.195224 | 0.739185 | 0.76097 | 0.743286 | 0.880073 | 0.722582 | 0.460159 | 0.787813 | 0.534152 | 0.904416 | 0.933144 |

R2 | 0.974704 | −2.47021 | 0.708873 | −2.61383 | 0.442114 | −18.7624 | −1.15938 | −0.95898 | −1.08704 | 0.401488 | −1.42774 | −12.0912 | −0.50122 | −7.56926 | 0.620112 | 0.766096 |

Pbias | 0.001949 | −0.24455 | −0.08746 | −0.24856 | −0.1201 | 2.935512 | −0.20827 | −0.19675 | −0.20589 | −0.12664 | −0.21778 | −0.3868 | −0.18123 | −0.33319 | −0.1022 | −0.07616 |

Year | Raw Data | GWSVR | Error | GM | Error | NGM | Error | DGM | Error | NDGM | Error | NGBM | Error | FGM | Error | FNGM | Error |

2015 | 1925 | 1962.351 | −37.3511 | 2286.432 | −361.432 | 2069.711 | −144.711 | 2297.588 | −372.588 | 2155.033 | −230.033 | 1468.653 | 456.3469 | 2281.862 | −356.862 | 2209.65 | −284.65 |

2016 | 2080.5 | 2177.216 | −96.7159 | 2654.098 | −573.598 | 2337.722 | −257.222 | 2667.468 | −586.968 | 2430.429 | −349.929 | 1300.472 | 780.0278 | 2614.386 | −533.886 | 2546.322 | −465.822 |

2017 | 2390.7 | 2415.607 | −24.9071 | 3080.887 | −690.187 | 2633.016 | −242.316 | 3096.894 | −706.194 | 2733.54 | −342.84 | 761.2975 | 1629.402 | 2989.801 | −599.101 | 2929.592 | −538.892 |

2018 | 2814.3 | 2680.101 | 134.1994 | 3576.305 | −762.005 | 2958.369 | −144.069 | 3595.452 | −781.152 | 3067.156 | −252.856 | 298.0357 | 2516.264 | 3413.731 | −599.431 | 3366.033 | −551.733 |

2019 | 3057.5 | 2973.554 | 83.94561 | 4151.389 | −1093.89 | 3316.842 | −259.342 | 4174.271 | −1116.77 | 3434.346 | −376.846 | 93.05441 | 2964.446 | 3892.534 | −835.034 | 3863.137 | −805.637 |

2020 | 3270.2 | 3299.14 | −28.9395 | 4818.947 | −1548.75 | 3711.806 | −441.606 | 4846.271 | −1576.07 | 3838.49 | −568.29 | 26.6897 | 3243.51 | 4433.394 | −1163.19 | 4429.442 | −1159.24 |

Minimum Error | −24.9071 | 361.4315 | 144.0692 | 372.5879 | 230.0328 | 456.3469 | 356.862 | 284.6495 | |||||||||

Maximum Error | 134.1994 | 1548.747 | 441.6064 | 1576.071 | 568.29 | 3243.51 | 1163.194 | 1159.242 | |||||||||

Median Error | −26.9233 | −726.096 | −249.769 | −743.673 | −346.384 | 2072.833 | −599.266 | −545.312 | |||||||||

Year | Raw Data | FDGM | Error | FNDGM | Error | NIPGM | Error | NIPNGM | Error | NIPDGM | Error | NIPNDGM | Error | FNGBM | Error | NIPNGBM | Error |

2015 | 1925 | 2283.078 | −358.078 | 2213.542 | −288.542 | 2296.67 | −371.67 | 2658.937 | −733.937 | 2246.545 | −321.545 | 2434.458 | −509.458 | 2241.281 | −316.281 | 2206.928 | −281.928 |

2016 | 2080.5 | 2612.751 | −532.251 | 2489.094 | −408.594 | 2636.458 | −555.958 | 3135.378 | −1054.88 | 2559.518 | −479.018 | 2871.576 | −791.076 | 2498.838 | −418.338 | 2451.233 | −370.733 |

2017 | 2390.7 | 2984.317 | −593.617 | 2783.428 | −392.728 | 3020.962 | −630.262 | 3706.396 | −1315.7 | 2909.279 | −518.579 | 3398.486 | −1007.79 | 2759.741 | −369.041 | 2694.717 | −304.017 |

2018 | 2814.3 | 3403.184 | −588.884 | 3096.616 | −282.316 | 3456.065 | −641.765 | 4393.209 | −1578.91 | 3300.153 | −485.853 | 4036.041 | −1221.74 | 3019.185 | −204.885 | 2932.474 | −118.174 |

2019 | 3057.5 | 3875.457 | −817.957 | 3428.723 | −371.223 | 3948.427 | −890.927 | 5221.795 | −2164.3 | 3736.97 | −679.47 | 4809.935 | −1752.44 | 3272.5 | −215 | 3160.051 | −102.551 |

2020 | 3270.2 | 4408.021 | −1137.82 | 3779.811 | −509.611 | 4505.582 | −1235.38 | 6223.96 | −2953.76 | 4225.133 | −954.933 | 5751.83 | −2481.63 | 3515.492 | −245.292 | 3373.759 | −103.559 |

Minimum Error | 358.078 | 282.3156 | 371.6699 | 733.9373 | 321.5452 | 509.4581 | 204.8847 | 102.5506 | |||||||||

Maximum Error | 1137.821 | 509.6112 | 1235.382 | 2953.76 | 954.933 | 2481.63 | 418.3375 | 370.7326 | |||||||||

Median Error | −591.251 | −381.975 | −636.013 | −1447.3 | −502.216 | −1114.76 | −280.787 | −200.051 |

Model | Model Abbreviation | Optimal Hyperparameters | Training Time (s) |
---|---|---|---|

Grey Wavelet Support Vector Regressor | GWSVR | C = 48.9141, $\nu $ = 0.2907 | 0.78489 s |

Nonhomogeneous Grey Bernoulli Model | NGBM | n = 4.2162 | 0.03195 s |

Fractional-order Grey Model | FGM | r = 0.1505 | 0.08470 s |

Fractional-order Nonhomogeneous Grey Model | FNGM | r = 1.0902 | 0.08795 s |

Fractional-order Discrete Grey Model | FDGM | r = 0.1354 | 0.08445 s |

Fractional-order Nonhomogeneous Discrete Grey Model | FNDGM | r = 0.7610 | 0.22519 s |

New Information Priority Grey Model | NIPGM | r = 0.8493 | 0.04955 s |

New Information Priority Nonhomogeneous Grey Model | NIPNGM | r = 0.9839 | 0.05257 s |

New Information Priority Discrete Grey Model | NIPDGM | r = 0.8481 | 0.04881 s |

New Information Priority Nonhomogeneous Discrete Grey Model | NIPNDGM | r = 0.9030 | 0.18856 s |

Nonlinear Grey Bernoulli Model with Fractional-order Accumulation | FNGBM | n = 2.0000, r = −1.0985 | 0.77943 s |

New Information Priority Nonlinear Grey Bernoulli Model | NIPNGBM | n = 2.0000, r = 0.0000 | 0.42851 s |

GWSVR | GM | NGM | DGM | NDGM | NGBM | FGM | FNGM | FDGM | FNDGM | NIPGM | NIPNGM | NIPDGM | NIPNDGM | FNGBM | NIPNGBM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

MAE | 70.73226 | 819.5432 | 257.3194 | 837.7925 | 360.0807 | 1867.335 | 715.0994 | 829.0862 | 693.1967 | 1083.294 | 625.0661 | 933.0845 | 626.1443 | 992.5079 | 245.9249 | 212.6661 |

MAPE | 2.757125 | 30.10073 | 9.851978 | 30.78744 | 13.84979 | 66.5773 | 26.7028 | 30.57583 | 25.96224 | 39.7818 | 23.46729 | 34.39759 | 23.52068 | 36.61266 | 9.073488 | 9.362476 |

MSE | 6665.947 | 803908.8 | 73997.96 | 838135.1 | 139421.4 | 4511417 | 579693.9 | 809827.3 | 540108.1 | 1399142 | 436994.9 | 1025289 | 437770.2 | 1156709 | 80026.38 | 59962.91 |

RMSE | 81.64525 | 896.6096 | 272.0257 | 915.4972 | 373.3918 | 2124.01 | 761.3763 | 899.9041 | 734.9205 | 1182.853 | 661.0559 | 1012.566 | 661.6421 | 1075.504 | 282.8893 | 244.8732 |

U1 | 0.015469 | 0.145006 | 0.048833 | 0.147606 | 0.065807 | 0.599254 | 0.125689 | 0.145397 | 0.12183 | 0.182914 | 0.110927 | 0.160705 | 0.111012 | 0.16901 | 0.054837 | 0.044542 |

U2 | 0.030774 | 0.337951 | 0.102532 | 0.34507 | 0.140739 | 0.800584 | 0.286979 | 0.339193 | 0.277007 | 0.445842 | 0.249166 | 0.381657 | 0.249387 | 0.40538 | 0.106627 | 0.092298 |

MedAe | 65.08349 | 718.5948 | 264.3201 | 736.1083 | 358.6198 | 1922.922 | 639.5297 | 735.5087 | 620.9857 | 963.1634 | 558.1595 | 831.6983 | 559.5865 | 886.4476 | 221.2416 | 207.2592 |

IA | 0.992839 | 0.695052 | 0.94051 | 0.688252 | 0.896817 | 0.213942 | 0.7358 | 0.691661 | 0.744624 | 0.605304 | 0.774006 | 0.654143 | 0.773404 | 0.633879 | 0.866851 | 0.932628 |

R2 | 0.974481 | −2.07761 | 0.716713 | −2.20864 | 0.466251 | −16.2711 | −1.21925 | −2.10027 | −1.0677 | −4.35635 | −0.67295 | −2.92513 | −0.67592 | −3.42824 | 0.693634 | 0.770443 |

Pbias | 0.00705 | −0.23943 | −0.08995 | −0.24346 | −0.12151 | 2.536972 | −0.21549 | −0.24154 | −0.21028 | −0.29384 | −0.19361 | −0.26385 | −0.19388 | −0.27601 | 0.044384 | −0.07552 |

Year | Raw Data | GWSVR | Error | GM | Error | NGM | Error | DGM | Error | NDGM | Error | NGBM | Error | FGM | Error | FNGM | Error |

2015 | 1931.8 | 1964.998 | −33.1984 | 2284.76 | −352.96 | 2078.055 | −146.255 | 2295.869 | −364.069 | 2161.222 | −229.422 | 1445.775 | 486.0255 | 2298.388 | −366.588 | 2304.295 | −372.495 |

2016 | 2078.1 | 2179.253 | −101.153 | 2651.476 | −573.376 | 2349.998 | −271.898 | 2664.793 | −586.693 | 2440.45 | −362.35 | 1266.439 | 811.6608 | 2639.455 | −561.355 | 2673.058 | −594.958 |

2017 | 2393.7 | 2416.87 | −23.1698 | 3077.053 | −683.353 | 2650.442 | −256.742 | 3093 | −699.3 | 2748.59 | −354.89 | 876.4395 | 1517.261 | 3025.939 | −632.239 | 3097.102 | −703.402 |

2018 | 2817.1 | 2680.395 | 136.705 | 3570.937 | −753.837 | 2982.374 | −165.274 | 3590.016 | −772.916 | 3088.635 | −271.535 | 488.517 | 2328.583 | 3463.921 | −646.821 | 3584.716 | −767.616 |

2019 | 3059.7 | 2972.654 | 87.04622 | 4144.092 | −1084.39 | 3349.095 | −289.395 | 4166.898 | −1107.2 | 3463.889 | −404.189 | 234.8974 | 2824.803 | 3960.299 | −900.599 | 4145.432 | −1085.73 |

2020 | 3339.9 | 3296.779 | 43.12077 | 4809.242 | −1469.34 | 3754.251 | −414.351 | 4836.479 | −1496.58 | 3877.998 | −538.098 | 104.2238 | 3235.676 | 4522.894 | −1182.99 | 4790.215 | −1450.32 |

Minimum Error | −23.1698 | −352.96 | −146.255 | −364.069 | −229.422 | 486.0255 | −366.588 | −372.495 | |||||||||

Maximum Error | 136.705 | −1469.34 | −414.351 | −1496.58 | −538.098 | 3235.676 | −1182.99 | −1450.32 | |||||||||

Median Error | 9.975466 | −718.595 | −264.32 | −736.108 | −358.62 | 1922.922 | −639.53 | −735.509 | |||||||||

Year | Raw Data | FDGM | Error | FNDGM | Error | NIPGM | Error | NIPNGM | Error | NIPDGM | Error | NIPNDGM | Error | FNGBM | Error | NIPNGBM | Error |

2015 | 1931.8 | 2298.021 | −366.221 | 2408.667 | −476.867 | 2266.683 | −334.883 | 2354.288 | −422.488 | 2269.313 | −337.513 | 2388.746 | −456.946 | 2122.463 | −190.663 | 2218.179 | −286.379 |

2016 | 2078.1 | 2633.393 | −555.293 | 2814.976 | −736.876 | 2591.951 | −513.851 | 2737.896 | −659.796 | 2594.262 | −516.162 | 2778.55 | −700.45 | 2269.559 | −191.459 | 2462.651 | −384.551 |

2017 | 2393.7 | 3012.31 | −618.61 | 3290.358 | −896.658 | 2957.806 | −564.106 | 3181.093 | −787.393 | 2959.606 | −565.906 | 3230.151 | −836.451 | 2417.44 | −23.7398 | 2706.616 | −312.916 |

2018 | 2817.1 | 3440.461 | −623.361 | 3846.769 | −1029.67 | 3369.313 | −552.213 | 3693.104 | −876.004 | 3370.367 | −553.267 | 3753.544 | −936.444 | 2566.076 | 251.0242 | 2945.239 | −128.139 |

2019 | 3059.7 | 3924.275 | −864.575 | 4498.21 | −1438.51 | 3832.167 | −772.467 | 4284.585 | −1224.88 | 3832.191 | −772.491 | 4360.338 | −1300.64 | 2715.438 | 344.262 | 3174.102 | −114.402 |

2020 | 3339.9 | 4471.02 | −1131.12 | 5261.082 | −1921.18 | 4352.777 | −1012.88 | 4967.842 | −1627.94 | 4351.427 | −1011.53 | 5064.019 | −1724.12 | 2865.499 | 474.4009 | 3389.509 | −49.6092 |

Minimum Error | −366.221 | −476.867 | −334.883 | −422.488 | −337.513 | −456.946 | −23.7398 | −49.6092 | |||||||||

Maximum Error | −1131.12 | −1921.18 | −1012.88 | −1627.94 | −1011.53 | −1724.12 | 474.4009 | −384.551 | |||||||||

Median Error | −620.986 | −963.163 | −558.16 | −831.698 | −559.586 | −886.448 | 113.6422 | −207.259 |

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## Share and Cite

**MDPI and ACS Style**

Ma, X.; Deng, Y.; Yuan, H.
Forecasting the Natural Gas Supply and Consumption in China Using a Novel Grey Wavelet Support Vector Regressor. *Systems* **2023**, *11*, 428.
https://doi.org/10.3390/systems11080428

**AMA Style**

Ma X, Deng Y, Yuan H.
Forecasting the Natural Gas Supply and Consumption in China Using a Novel Grey Wavelet Support Vector Regressor. *Systems*. 2023; 11(8):428.
https://doi.org/10.3390/systems11080428

**Chicago/Turabian Style**

Ma, Xin, Yanqiao Deng, and Hong Yuan.
2023. "Forecasting the Natural Gas Supply and Consumption in China Using a Novel Grey Wavelet Support Vector Regressor" *Systems* 11, no. 8: 428.
https://doi.org/10.3390/systems11080428