x = apply.monthly(xts(data1$Sales,order.by = date1),FUN = sum)
x = as.data.frame(x)
x1 = ts(x,frequency = 12,start = c(2011,1),end = c(2014,12))
hchart(forecast(auto.arima(x1)))
因此，预测输出为：
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 Jan 2015 358371.9 315315.3 401428.5 292522.5 424221.3 Feb 2015 323524.1 276176.7 370871.6 251112.4 395935.8 Mar 2015 381082.9 329802.3 432363.4 302656.1 459509.7 Apr 2015 362998.6 308065.9 417931.3 278986.3 447011.0 May 2015 415109.8 356753.0 473466.6 325860.8 504358.8 Jun 2015 509654.5 448063.7 571245.3 415459.5 603849.5 Jul 2015 377217.7 312554.5 441881.0 278323.9 476111.6 Aug 2015 517245.5 449649.3 584841.6 413866.0 620624.9 Sep 2015 552086.9 481679.9 622494.0 444408.6 659765.2 Oct 2015 485304.4 412194.4 558414.3 373492.4 597116.4 Nov 2015 583234.5 507518.1 658950.9 467436.3 699032.8 Dec 2015 586645.4 508409.3 664881.5 466993.6 706297.1 Jan 2016 424985.8 340008.8 509962.7 295024.7 554946.8 Feb 2016 390138.0 301632.4 478643.7 254780.3 525495.7 Mar 2016 447696.7 355797.8 539595.7 307149.4 588244.0 Apr 2016 429612.5 334441.2 524783.8 284060.5 575164.5 May 2016 481723.7 383388.8 580058.6 331333.5 632113.9 Jun 2016 576268.4 474868.6 677668.2 421190.8 731346.0 Jul 2016 443831.6 339456.9 548206.3 284204.3 603459.0 Aug 2016 583859.3 476592.2 691126.5 419808.3 747910.3 Sep 2016 618700.8 508617.1 728784.5 450342.4 787059.3 Oct 2016 551918.3 439088.4 664748.1 379359.8 724476.7 Nov 2016 649848.4 534337.6 765359.2 473189.9 826507.0 Dec 2016 653259.3 535128.3 771390.2 472593.6 833924.9
因此，对于上面的数据，highchart工作正常，但是当我尝试使用TBATS函数的每周数据时，它会给出类似下面的内容，
x1 = ts(x,freq = 365.25/7,start = 2011+31/365.25)
bestfit <- list(aicc=Inf)
fitmodel <- tbats(x1)
forecastweekly <- forecast(fitmodel, h=200)
并预测输出为：
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 2015.110 77239.91 62831.38 91648.44 55203.96 99275.86 2015.129 71852.27 57413.72 86290.82 49770.41 93934.13 2015.148 71560.14 57042.50 86077.79 49357.32 93762.96 2015.167 73289.48 58602.92 87976.04 50828.33 95750.64 2015.186 73652.31 58732.79 88571.84 50834.87 96469.76 2015.205 71700.22 56559.47 86840.98 48544.43 94856.02 2015.225 69387.13 54075.57 84698.69 45970.11 92804.14 2015.244 69554.04 54104.26 85003.83 45925.64 93182.45 2015.263 73306.79 57716.77 88896.82 49463.91 97149.68 2015.282 78975.88 63224.05 94727.71 54885.53 103066.23 2015.301 83368.51 67442.71 99294.31 59012.10 107724.93 2015.320 84148.31 68060.33 100236.30 59543.86 108752.76 2015.339 81426.24 65196.75 97655.74 56605.37 106247.11 2015.359 77443.50 61081.10 93805.90 52419.37 102467.63 2015.378 74775.69 58272.42 91278.97 49536.12 100015.27 2015.397 74558.00 57901.69 91214.32 49084.37 100031.64 2015.416 76095.25 59285.54 92904.96 50387.01 101803.48 2015.435 78060.88 61110.28 95011.48 52137.18 103984.59 2015.454 80180.84 63100.97 97260.72 54059.43 106302.25 2015.474 83822.54 66613.41 101031.67 57503.44 110141.64 2015.493 90712.50 73365.03 108059.98 64181.84 117243.17 2015.512 100614.62 83122.87 118106.38 73863.30 127365.95 2015.531 109996.21 92365.73 127626.69 83032.72 136959.70 2015.550 113344.54 95586.69 131102.39 86186.25 140502.83 2015.569 106822.66 88942.86 124702.47 79477.86 134167.47 2015.589 91762.34 73755.78 109768.90 64223.68 119301.00 2015.608 75016.97 56875.59 93158.36 47272.12 102761.83 2015.627 65240.68 46964.00 83517.37 37288.90 93192.47 2015.646 67389.62 48986.56 85792.69 39244.57 95534.68 2015.665 79501.06 60980.53 98021.58 51176.36 107825.75 2015.684 94341.82 75703.92 112979.72 65837.61 122846.03 2015.704 104738.56 85975.61 123501.51 76043.10 133434.01 2015.723 108388.65 89495.21 127282.10 79493.63 137283.68 2015.742 108375.06 89355.88 127394.24 79287.74 137462.38 2015.761 109260.33 90126.03 128394.63 79996.94 138523.71 2015.780 112408.56 93164.45 131652.67 82977.23 141839.89 2015.799 114746.16 95387.18 134105.13 85139.16 144353.16 2015.819 111991.74 92508.83 131474.64 82195.20 141788.27 2015.838 103303.51 83695.64 122911.38 73315.86 133291.16 2015.857 92995.07 73272.48 112717.66 62831.97 123158.17 2015.876 87624.60 67798.21 107450.99 57302.76 117946.44 2015.895 90935.62 71005.54 110865.70 60455.19 121416.04 2015.914 101024.88 80980.22 121069.54 70369.21 131680.54 2015.934 112068.57 91900.42 132236.72 81224.05 142913.10 2015.953 118900.25 98615.37 139185.12 87877.21 149923.29 2015.972 120396.94 100013.10 140780.77 89222.55 151571.32 2015.991 119001.71 98529.05 139474.37 87691.47 150311.95 2016.010 117346.47 96775.77 137917.18 85886.30 148806.65 2016.029 115418.48 94727.90 136109.06 83774.97 147061.99 2016.049 110873.10 90056.68 131689.51 79037.14 142709.05 2016.068 101934.77 81024.78 122844.76 69955.70 133913.84 2016.087 89905.85 68950.05 110861.65 57856.72 121954.98 2016.106 78799.43 57824.70 99774.16 46721.35 110877.51 2016.125 72389.82 51396.94 93382.69 40283.98 104495.65 2016.144 71351.98 50313.37 92390.59 39176.20 103527.76 2016.164 73003.82 51860.00 94147.65 40667.13 105340.51 2016.183 73773.35 52471.74 95074.96 41195.35 106351.36 2016.202 72159.45 50697.92 93620.97 39336.88 104982.02 2016.221 69690.29 48101.92 91278.66 36673.73 102706.85 2016.240 69256.12 47567.38 90944.87 36086.05 102426.20 2016.259 72415.52 50628.61 94202.44 39095.31 105735.74 2016.279 77962.67 56062.75 99862.59 44469.63 111455.71 2016.298 82809.41 60784.56 104834.26 49125.31 116493.51 2016.317 84304.26 62159.57 106448.94 50436.88 118171.63 2016.336 82086.07 59836.17 104335.97 48057.78 116114.36 2016.355 78121.18 55774.15 100468.21 43944.35 112298.01 2016.374 75078.43 52629.85 97527.01 40746.29 109410.57 2016.394 74429.74 51869.81 96989.66 39927.31 108932.17 2016.413 75756.42 53082.36 98430.48 41079.43 110433.41 2016.432 77713.76 54933.27 100494.26 42874.00 112553.53 2016.451 79746.92 56869.18 102624.66 44758.44 114735.40 2016.470 82971.40 59997.81 105944.99 47836.33 118106.47 2016.489 89212.90 66136.98 112288.81 53921.33 124504.46 2016.509 98741.12 75556.92 121925.32 63283.95 134198.29 2016.528 108602.08 85311.87 131892.29 72982.78 144221.39 2016.547 113413.17 90025.09 136801.25 77644.19 149182.15 2016.566 108734.00 85253.17 132214.84 72823.16 144644.85 2016.585 94827.00 71250.69 118403.30 58770.15 130883.84 2016.604 77727.28 54048.85 101405.70 41514.25 113940.30 2016.624 66170.44 42387.77 89953.11 29797.98 102542.90 2016.643 66126.31 42245.02 90007.61 29603.02 102649.60 2016.662 76895.42 52922.76 100868.09 40232.39 113558.45 2016.681 91855.81 67793.01 115918.60 55054.94 128656.67 2016.700 103392.61 79234.14 127551.07 66445.42 140339.79 2016.719 108144.16 83884.58 132403.74 71042.33 145245.99 2016.739 108422.65 84063.92 132781.39 71169.18 145676.13 2016.758 108911.01 84460.96 133361.05 71517.89 146304.12 2016.777 111773.52 87237.39 136309.66 74248.74 149298.30 2016.796 114600.56 89975.52 139225.60 76939.81 152261.31 2016.815 112965.28 88243.86 137686.70 75157.14 150773.43 2016.834 105155.15 80334.64 129975.66 67195.46 143114.84 2016.854 94653.85 69740.89 119566.80 56552.78 132754.92 2016.873 87985.16 62988.95 112981.38 49756.75 126213.58 2016.892 89719.06 64641.36 114796.75 51366.03 128072.08 2016.911 98965.92 73798.98 124132.85 60476.41 137455.42 2016.930 110299.28 85034.53 135564.04 71660.17 148938.39 2016.949 118099.93 92740.04 143459.82 79315.33 156884.53 2016.969 120437.17 94995.61 145878.74 81527.65 159346.69 2016.988 119320.41 93807.25 144833.57 80301.40 158339.42 2017.007 117619.63 92030.25 143209.00 78484.06 156755.19 2017.026 115874.87 90192.15 141557.58 76596.54 155153.19 2017.045 111987.83 86202.47 137773.20 72552.52 151423.15 2017.064 103841.95 77974.49 129709.41 64281.08 143402.82 2017.084 92119.32 66208.61 118030.03 52492.32 131746.33
因此，对于上述预测输出，高图功能无法正常工作，例如：
hchart(forecastweekly)
它给出错误：
as.Date.ts（。）出错：无法将ts时间转换为Date类
但是当我使用绘图功能时，它会提供正确的输出。我该如何处理这个错误？
在这里，你可以看到一个可重复的例子，取自Rob J. Hyndman的website
library(forecast)
gas <- ts(read.csv("http://robjhyndman.com/data/gasoline.csv", header=FALSE)
[,1], freq=365.25/7, start=1991+31/365.25)
gastbats <- tbats(gas)
fc2 <- forecast(gastbats, h=104)
plot(fc2) #It works
hchart(fc2) #It doesn't
问候！