预测方法包括插值拟合 灰色预测 回归分析 马尔可夫预测 神经网络预测 时间序列的详细介绍

X=[598.00 349.00 461.00 57482.00 20729.00 44.00 586.00 455.00 475.00 58796.00 21364.00 89.00 707.00 520.00 491.00 60266.00 21832.00 97.00 737.00 558.00 529.00 61465.00 22328.00 98.00 825.00 715.00 556.00 62828.00 23018.00 150.00 837.00 798.00 575.00 64653.00 23711.00 139.00 1028.00 1235.00 598.00 65994.00 26600.00 256.00 1114.00 1681.00 509.00 67207.00 26173.00 338.00 1079.00 1870.00 444.00 66207.00 25880.00 380.00 757.00 1156.00 434.00 65859.00 25590.00 138.00 677.00 964.00 461.00 67295.00 25110.00 66.00 779.00 1046.00 514.00 69172.00 26640.00 85.00 943.00 1250.00 584.00 70499.00 27736.00 129.00 1152.00 1581.00 632.00 72538.00 28670.00 175.00 1322.00 1911.00 687.00 74542.00 29805.00 212.00 1249.00 1647.00 697.00 76368.00 30814.00 156.00 1187.00 1565.00 680.00 78534.00 31915.00 127.00 1372.00 2101.00 688.00 80671.00 33225.00 207.00 1638.00 2747.00 767.00 82992.00 34432.00 312.00 1780.00 3156.00 790.00 85229.00 35620.00 355.00 1833.00 3365.00 789.00 87177.00 35854.00 354.00 1978.00 3684.00 855.00 89211.00 36652.00 374.00 1993.00 3696.00 891.00 90859.00 37369.00 393.00 2121.00 4254.00 932.00 92421.00 38168.00 462.00 2052.00 4309.00 955.00 93717.00 38834.00 443.00 2189.00 4925.00 971.00 94974.00 39377.00 454.00 2475.00 5590.00 1058.00 96259.00 39856.00 550.00 2702.00 6065.00 1150.00 97542.00 40581.00 564.00 2791.00 6592.00 1194.00 98705.00 41896.00 568.00 2927.00 6862.00 1273.00 100072.0 43280.00 496.00]; y=[184.00 216.00 248.00 254.00 268.00 286.00 357.00 444.00 506.00 ... 271.00 230.00 266.00 323.00 393.00 466.00 352.00 303.00 447.00 ... 564.00 638.00 658.00 691.00 655.00 692.00 657.00 723.00 922.00 ... 890.00 826.00 810.0]'; beta0=[0.50 -0.03 -0.60 0.01 -0.02 0.35]; betafit = nlinfit(X,y,'model',beta0)