如何使用 plotly 为具有 'Index' 和 Datetime 值的时间序列数据绘制预测
How to plot forecast using plotly for time series data having 'Index' with Datetime value
我想使用 plotly 绘制时间序列的预测图。但我的时间序列索引由时间戳 [日期时间] 值组成。这使得很难绘制预测部分。
> qxts
[,1]
2017-04-25 16:52:00 -0.4120000
2017-04-25 16:53:00 -0.4526667
2017-04-25 16:54:00 -0.4586667
2017-04-25 16:55:00 -0.4606667
2017-04-25 16:56:00 -0.5053333
2017-04-25 16:57:00 -0.5066667
2017-04-25 16:58:00 -0.5100000
2017-04-25 16:59:00 -0.4986667
2017-04-25 17:00:00 -0.5026667
2017-04-25 17:01:00 -0.5053333
2017-04-25 17:02:00 -0.5020000
2017-04-25 17:03:00 -0.5066667
2017-04-25 17:04:00 -0.5080000
2017-04-25 17:05:00 -0.5040000
2017-04-25 17:06:00 -0.5013333
2017-04-25 17:07:00 -0.5020000
2017-04-25 17:08:00 -0.5020000
2017-04-25 17:09:00 -0.5040000
2017-04-25 17:10:00 -0.5046667
2017-04-25 17:11:00 -0.5053333
2017-04-25 17:12:00 -0.5013333
2017-04-25 17:13:00 -0.5033333
2017-04-25 17:14:00 -0.4966667
2017-04-25 17:15:00 -0.5040000
2017-04-25 17:16:00 -0.4980000
2017-04-25 17:17:00 -0.3893333
2017-04-25 17:18:00 -0.3640000
2017-04-25 17:19:00 -0.3653333
2017-04-25 17:20:00 -0.3586667
2017-04-25 17:21:00 -0.3600000
2017-04-25 17:22:00 -0.3553333
2017-04-25 17:23:00 -0.3653333
2017-04-25 17:24:00 -0.3606667
2017-04-25 17:25:00 -0.3640000
2017-04-25 17:26:00 -0.3546667
2017-04-25 17:27:00 -0.3700000
2017-04-25 17:28:00 -0.3653333
2017-04-25 17:29:00 -0.3673333
2017-04-25 17:30:00 -0.3966667
2017-04-25 17:31:00 -0.4693333
2017-04-25 17:32:00 -0.4586667
2017-04-25 17:33:00 -0.4640000
2017-04-25 17:34:00 -0.4573333
2017-04-25 17:35:00 -0.4646667
2017-04-25 17:36:00 -0.4680000
2017-04-25 17:37:00 -0.4688000
2017-04-25 17:38:00 -0.4673333
2017-04-25 17:39:00 -0.4606667
2017-04-25 17:40:00 -0.4653333
2017-04-25 17:41:00 -0.4720000
2017-04-25 17:42:00 -0.4533333
2017-04-25 17:43:00 -0.4660000
2017-04-25 17:44:00 -0.4646667
2017-04-25 17:45:00 -0.4593333
2017-04-25 17:46:00 -0.4646667
2017-04-25 17:47:00 -0.4580000
2017-04-25 17:48:00 -0.4646667
2017-04-25 17:49:00 -0.4740000
2017-04-25 17:50:00 -0.4173333
2017-04-25 17:51:00 -0.4640000
2017-04-25 17:52:00 -0.4600000
2017-04-25 17:53:00 -0.4686667
2017-04-25 17:54:00 -0.4720000
2017-04-25 17:55:00 -0.4653333
2017-04-25 17:56:00 -0.4720000
2017-04-25 17:57:00 -0.4626667
2017-04-25 17:58:00 -0.4624000
2017-04-25 17:59:00 -0.4653333
2017-04-25 18:00:00 -0.4673333
2017-04-25 18:01:00 -0.4600000
2017-04-25 18:02:00 -0.4646667
2017-04-25 18:03:00 -0.4613333
2017-04-25 18:04:00 -0.4660000
2017-04-25 18:05:00 -0.4620000
2017-04-25 18:06:00 -0.4620000
2017-04-25 18:07:00 -0.4666667
2017-04-25 18:08:00 -0.4573333
2017-04-25 18:09:00 -0.4660000
2017-04-25 18:10:00 -0.4640000
2017-04-25 18:11:00 -0.4640000
2017-04-25 18:12:00 -0.4886667
2017-04-25 18:13:00 -0.5433333
2017-04-25 18:14:00 -0.4893333
2017-04-25 18:15:00 -0.3600000
2017-04-25 18:16:00 0.0000000
2017-04-25 18:17:00 0.0000000
2017-04-25 18:18:00 0.0000000
2017-04-25 18:19:00 0.0000000
2017-04-25 18:20:00 0.0000000
2017-04-25 18:21:00 0.0000000
2017-04-25 18:22:00 0.0000000
2017-04-25 18:23:00 0.0000000
2017-04-25 18:24:00 0.0000000
2017-04-25 18:25:00 0.0000000
2017-04-25 18:26:00 0.0000000
2017-04-25 18:27:00 0.0000000
2017-04-25 18:28:00 0.0000000
2017-04-25 18:29:00 0.0000000
2017-04-25 18:30:00 0.0000000
2017-04-25 18:31:00 0.0000000
2017-04-25 18:32:00 0.0000000
2017-04-25 18:33:00 0.0000000
2017-04-25 18:34:00 0.0000000
2017-04-25 18:35:00 0.0000000
2017-04-25 18:36:00 -0.3573333
2017-04-25 18:37:00 -0.4646667
2017-04-25 18:38:00 -0.3360000
2017-04-25 18:39:00 -0.4453333
2017-04-25 18:40:00 -0.4933333
2017-04-25 18:41:00 -0.4446667
2017-04-25 18:42:00 -0.3253333
2017-04-25 18:43:00 -0.6020000
2017-04-25 18:44:00 -0.3304000
2017-04-25 18:45:00 -0.3946667
2017-04-25 18:46:00 -0.4066667
2017-04-25 18:47:00 -0.4066667
2017-04-25 18:48:00 -0.5253333
2017-04-25 18:49:00 -0.5520000
2017-04-25 18:50:00 -0.6973333
2017-04-25 18:51:00 -0.5260000
2017-04-25 18:52:00 -0.5653333
2017-04-25 18:53:00 -0.4514286
2017-04-25 18:54:00 -0.3973333
2017-04-25 18:55:00 -0.5920000
2017-04-25 18:56:00 -0.4912000
2017-04-25 18:57:00 -0.4313333
2017-04-25 18:58:00 -0.1973333
2017-04-25 18:59:00 -0.5460000
2017-04-25 19:00:00 -0.4960000
2017-04-25 19:01:00 -0.4595000
2017-04-25 19:02:00 -0.4592000
2017-04-25 19:03:00 -0.4537143
2017-04-25 19:04:00 -0.4553333
2017-04-25 19:05:00 -0.4613333
2017-04-25 19:06:00 -0.4620000
2017-04-25 19:07:00 -0.4573333
2017-04-25 19:08:00 -0.4606667
2017-04-25 19:09:00 -0.4560000
2017-04-25 19:10:00 -0.4593333
2017-04-25 19:11:00 -0.4580000
2017-04-25 19:12:00 -0.4568000
2017-04-25 19:13:00 -0.4554286
2017-04-25 19:14:00 -0.4546667
2017-04-25 19:15:00 -0.4540000
2017-04-25 19:16:00 -0.4526667
2017-04-25 19:17:00 -0.4640000
2017-04-25 19:18:00 -0.4566667
2017-04-25 19:19:00 -0.4544000
2017-04-25 19:20:00 -0.4566667
2017-04-25 19:21:00 -0.4546667
2017-04-25 19:22:00 -0.4600000
2017-04-25 19:23:00 -0.4584000
2017-04-25 19:24:00 -0.4593333
2017-04-25 19:25:00 -0.3946667
2017-04-25 19:26:00 -0.3673333
2017-04-25 19:27:00 -0.2824000
2017-04-25 19:28:00 -0.3446667
2017-04-25 19:29:00 -0.4646667
2017-04-25 19:30:00 -0.4433333
2017-04-25 19:31:00 -0.3392000
2017-04-25 19:32:00 -0.3326667
2017-04-25 19:33:00 -0.3633333
2017-04-25 19:34:00 -0.4360000
2017-04-25 19:35:00 -0.5180000
2017-04-25 19:36:00 -0.1593333
2017-04-25 19:37:00 -0.2493333
2017-04-25 19:38:00 -0.4336000
2017-04-25 19:39:00 -0.4626667
2017-04-25 19:40:00 -0.4100000
2017-04-25 19:41:00 -0.3573333
2017-04-25 19:42:00 -0.3616000
2017-04-25 19:43:00 -0.4946667
2017-04-25 19:44:00 -0.5220000
2017-04-25 19:45:00 -0.2940000
2017-04-25 19:46:00 -0.4528000
2017-04-25 19:47:00 -0.4740000
2017-04-25 19:48:00 -0.3426667
2017-04-25 19:49:00 -0.5368000
2017-04-25 19:50:00 -0.4233333
2017-04-25 19:51:00 -0.4986667
2017-04-25 19:52:00 -0.4393333
2017-04-25 19:53:00 -0.4648000
2017-04-25 19:54:00 -0.3686667
2017-04-25 19:55:00 -0.2673333
2017-04-25 19:56:00 -0.3033333
2017-04-25 19:57:00 -0.2128000
2017-04-25 19:58:00 -0.3980000
2017-04-25 19:59:00 -0.4446667
2017-04-25 20:00:00 -0.6368000
2017-04-25 20:01:00 -0.3373333
2017-04-25 20:02:00 -0.2600000
预测我的时间序列
fit.xts <- auto.arima(qxts)
fore.xts <- forecast(fit.xts, h=10)
绘制预测图
plot_ly() %>% add_lines(x = ~time(qxts), y = ~qxts,
color = I("black"), name = "observed") %>%
add_ribbons(x = time(fore.xts$mean), ymin = fore.xts$lower[, 2], ymax =
fore.xts$upper[, 2],
color = I("gray95"), name = "95% confidence") %>%
add_ribbons(x = time(fore.xts$mean), ymin = fore.xts$lower[, 1], ymax =
fore.xts$upper[, 1],color = I("gray80"), name = "80% confidence") %>%
add_lines(x = time(fore.xts$mean), y = fore.xts$mean, color = I("blue"), name = "prediction")
这给出了空图。有人能帮我吗？这两天一直在找答案
dput(qxts) structure(c(-0.4120000005, -0.452666665083333, -0.458666667316667,
-0.460666666416667, -0.505333344133333, -0.506666670233333, -0.50999999045,
-0.498666668933333, -0.502666672066667, -0.505333344133333, -0.50200000405,
-0.506666680166667, -0.508000006266667, -0.5040000081, -0.501333341,
-0.50200000405, -0.502000009016667, -0.504000003133333, -0.50466667115,
-0.505333344133333, -0.501333341, -0.503333340083333, -0.496666664883333,
-0.5040000081, -0.49799999595, -0.389333337533333, -0.3640000075,
-0.365333338566667, -0.3586666733, -0.3599999994, -0.355333338183333,
-0.365333338566667, -0.360666667416667, -0.3640000075, -0.3546666652,
-0.369999999783333, -0.3653333336, -0.36733333765, -0.396666670833333,
-0.469333335766667, -0.4586666673, -0.4639999916, -0.457333331283333,
-0.4646666596, -0.4680000047, -0.4687999964, -0.467333336683333,
-0.460666661433333, -0.4653333326, -0.4720000029, -0.453333328166667,
-0.466000000616667, -0.4646666646, -0.45933333535, -0.464666659633333,
-0.4579999993, -0.464666669566667, -0.473999996983333, -0.417333334666667,
-0.46400000155, -0.4599999934, -0.46866666775, -0.47200000288,
-0.465333337583333, -0.4719999979, -0.462666660516667, -0.46240000126,
-0.465333322666667, -0.4673333317, -0.459999993416667, -0.464666659616667,
-0.461333329466667, -0.46599999565, -0.461999997483333, -0.461999992516667,
-0.466666663683333, -0.457333336283333, -0.46600000065, -0.463999996566667,
-0.46400000155, -0.488666658566667, -0.543333326766667, -0.489333341533333,
-0.3600000143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3573333323, -0.464666662133333, -0.3360000037,
-0.445333333066667, -0.49333334465, -0.444666661316667, -0.325333339483333,
-0.60199998815, -0.33039999454, -0.394666674216667, -0.406666671233333,
-0.4066666588, -0.52533333005, -0.552000006066667, -0.6973333458,
-0.526000003, -0.565333336583333, -0.451428575185714, -0.3973333314,
-0.591999994871429, -0.49120000004, -0.4313333432, -0.197333325966667,
-0.545999998833333, -0.49600000976, -0.4595000035875, -0.45920000072,
-0.453714281285714, -0.455333337166667, -0.461333334433333, -0.461999992533333,
-0.457333331283333, -0.4606666664, -0.456000005183333, -0.4593333254,
-0.4579999993, -0.45679999586, -0.455428570485714, -0.454666664183333,
-0.453999991183333, -0.452666665083333, -0.464000001533333, -0.456666668216667,
-0.45439999696, -0.45666666825, -0.454666664166667, -0.459999998383333,
-0.45840000508, -0.459333330366667, -0.394666661816667, -0.367333332983333,
-0.2823999971, -0.3446666648, -0.464666659633333, -0.443333327766667,
-0.33920000494, -0.332666660333333, -0.363333340716667, -0.43599999546,
-0.518000001716667, -0.15933333585, -0.2493333357, -0.43359999356,
-0.462666669166667, -0.410000008833333, -0.357333337883333, -0.3615999937,
-0.494666665783333, -0.52200000485, -0.29400000225, -0.4527999997,
-0.474000004416667, -0.342666663233333, -0.53680000306, -0.423333331933333,
-0.498666671416667, -0.439333344516667, -0.46480000914, -0.368666668733333,
-0.2673333399, -0.303333333366667, -0.21279999912, -0.398000004383333,
-0.444666676216667, -0.63679999114, -0.337333336466667, -0.2599999905 ), .Dim = c(191L, 1L), index = structure(c(1493139120, 1493139180,
1493139240, 1493139300, 1493139360, 1493139420, 1493139480,
1493139540, 1493139600, 1493139660, 1493139720, 1493139780,
1493139840, 1493139900, 1493139960, 1493140020, 1493140080,
1493140140, 1493140200, 1493140260, 1493140320, 1493140380,
1493140440, 1493140500, 1493140560, 1493140620, 1493140680,
1493140740, 1493140800, 1493140860, 1493140920, 1493140980,
1493141040, 1493141100, 1493141160, 1493141220, 1493141280,
1493141340, 1493141400, 1493141460, 1493141520, 1493141580,
1493141640, 1493141700, 1493141760, 1493141820, 1493141880,
1493141940, 1493142000, 1493142060, 1493142120, 1493142180,
1493142240, 1493142300, 1493142360, 1493142420, 1493142480,
1493142540, 1493142600, 1493142660, 1493142720, 1493142780,
1493142840, 1493142900, 1493142960, 1493143020, 1493143080,
1493143140, 1493143200, 1493143260, 1493143320, 1493143380,
1493143440, 1493143500, 1493143560, 1493143620, 1493143680,
1493143740, 1493143800, 1493143860, 1493143920, 1493143980,
1493144040, 1493144100, 1493144160, 1493144220, 1493144280,
1493144340, 1493144400, 1493144460, 1493144520, 1493144580,
1493144640, 1493144700, 1493144760, 1493144820, 1493144880,
1493144940, 1493145000, 1493145060, 1493145120, 1493145180,
1493145240, 1493145300, 1493145360, 1493145420, 1493145480,
1493145540, 1493145600, 1493145660, 1493145720, 1493145780,
1493145840, 1493145900, 1493145960, 1493146020, 1493146080,
1493146140, 1493146200, 1493146260, 1493146320, 1493146380,
1493146440, 1493146500, 1493146560, 1493146620, 1493146680,
1493146740, 1493146800, 1493146860, 1493146920, 1493146980,
1493147040, 1493147100, 1493147160, 1493147220, 1493147280,
1493147340, 1493147400, 1493147460, 1493147520, 1493147580,
1493147640, 1493147700, 1493147760, 1493147820, 1493147880,
1493147940, 1493148000, 1493148060, 1493148120, 1493148180,
1493148240, 1493148300, 1493148360, 1493148420, 1493148480,
1493148540, 1493148600, 1493148660, 1493148720, 1493148780,
1493148840, 1493148900, 1493148960, 1493149020, 1493149080,
1493149140, 1493149200, 1493149260, 1493149320, 1493149380,
1493149440, 1493149500, 1493149560, 1493149620, 1493149680,
1493149740, 1493149800, 1493149860, 1493149920, 1493149980,
1493150040, 1493150100, 1493150160, 1493150220, 1493150280,
1493150340, 1493150400, 1493150460, 1493150520), tzone = "", tclass =
c("POSIXct", "POSIXt")), class = c("xts", "zoo"), .indexCLASS =
c("POSIXct", "POSIXt"), tclass = c("POSIXct", "POSIXt"), .indexTZ =
"", tzone = "")
您可以通过 as.POSIXct 转换您的时间戳，然后绘制它。对于预测，您还需要提供日期（在以下示例中，我假设时差始终相等）。
library(forecast)
qxts = structure(list(y=c(-0.4120000005, -0.452666665083333, -0.458666667316667, -0.460666666416667, -0.505333344133333, -0.506666670233333, -0.50999999045, -0.498666668933333, -0.502666672066667, -0.505333344133333, -0.50200000405, -0.506666680166667, -0.508000006266667, -0.5040000081, -0.501333341, -0.50200000405, -0.502000009016667, -0.504000003133333, -0.50466667115, -0.505333344133333, -0.501333341, -0.503333340083333, -0.496666664883333, -0.5040000081, -0.49799999595, -0.389333337533333, -0.3640000075, -0.365333338566667, -0.3586666733, -0.3599999994, -0.355333338183333, -0.365333338566667, -0.360666667416667, -0.3640000075, -0.3546666652, -0.369999999783333, -0.3653333336, -0.36733333765, -0.396666670833333, -0.469333335766667, -0.4586666673, -0.4639999916, -0.457333331283333, -0.4646666596, -0.4680000047, -0.4687999964, -0.467333336683333, -0.460666661433333, -0.4653333326, -0.4720000029, -0.453333328166667, -0.466000000616667, -0.4646666646, -0.45933333535, -0.464666659633333, -0.4579999993, -0.464666669566667, -0.473999996983333, -0.417333334666667, -0.46400000155, -0.4599999934, -0.46866666775, -0.47200000288, -0.465333337583333, -0.4719999979, -0.462666660516667, -0.46240000126, -0.465333322666667, -0.4673333317, -0.459999993416667, -0.464666659616667, -0.461333329466667, -0.46599999565, -0.461999997483333, -0.461999992516667, -0.466666663683333, -0.457333336283333, -0.46600000065, -0.463999996566667, -0.46400000155, -0.488666658566667, -0.543333326766667, -0.489333341533333, -0.3600000143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3573333323, -0.464666662133333, -0.3360000037, -0.445333333066667, -0.49333334465, -0.444666661316667, -0.325333339483333, -0.60199998815, -0.33039999454, -0.394666674216667, -0.406666671233333, -0.4066666588, -0.52533333005, -0.552000006066667, -0.6973333458, -0.526000003, -0.565333336583333, -0.451428575185714, -0.3973333314, -0.591999994871429, -0.49120000004, -0.4313333432, -0.197333325966667, -0.545999998833333, -0.49600000976, -0.4595000035875, -0.45920000072, -0.453714281285714, -0.455333337166667, -0.461333334433333, -0.461999992533333, -0.457333331283333, -0.4606666664, -0.456000005183333, -0.4593333254, -0.4579999993, -0.45679999586, -0.455428570485714, -0.454666664183333, -0.453999991183333, -0.452666665083333, -0.464000001533333, -0.456666668216667, -0.45439999696, -0.45666666825, -0.454666664166667, -0.459999998383333, -0.45840000508, -0.459333330366667, -0.394666661816667, -0.367333332983333, -0.2823999971, -0.3446666648, -0.464666659633333, -0.443333327766667, -0.33920000494, -0.332666660333333, -0.363333340716667, -0.43599999546, -0.518000001716667, -0.15933333585, -0.2493333357, -0.43359999356, -0.462666669166667, -0.410000008833333, -0.357333337883333, -0.3615999937, -0.494666665783333, -0.52200000485, -0.29400000225, -0.4527999997, -0.474000004416667, -0.342666663233333, -0.53680000306, -0.423333331933333, -0.498666671416667, -0.439333344516667, -0.46480000914, -0.368666668733333, -0.2673333399, -0.303333333366667, -0.21279999912, -0.398000004383333, -0.444666676216667, -0.63679999114, -0.337333336466667, -0.2599999905 ),
date = c(1493139120, 1493139180, 1493139240, 1493139300, 1493139360, 1493139420, 1493139480, 1493139540, 1493139600, 1493139660, 1493139720, 1493139780, 1493139840, 1493139900, 1493139960, 1493140020, 1493140080, 1493140140, 1493140200, 1493140260, 1493140320, 1493140380, 1493140440, 1493140500, 1493140560, 1493140620, 1493140680, 1493140740, 1493140800, 1493140860, 1493140920, 1493140980, 1493141040, 1493141100, 1493141160, 1493141220, 1493141280, 1493141340, 1493141400, 1493141460, 1493141520, 1493141580, 1493141640, 1493141700, 1493141760, 1493141820, 1493141880, 1493141940, 1493142000, 1493142060, 1493142120, 1493142180, 1493142240, 1493142300, 1493142360, 1493142420, 1493142480, 1493142540, 1493142600, 1493142660, 1493142720, 1493142780, 1493142840, 1493142900, 1493142960, 1493143020, 1493143080, 1493143140, 1493143200, 1493143260, 1493143320, 1493143380, 1493143440, 1493143500, 1493143560, 1493143620, 1493143680, 1493143740, 1493143800, 1493143860, 1493143920, 1493143980, 1493144040, 1493144100, 1493144160, 1493144220, 1493144280, 1493144340, 1493144400, 1493144460, 1493144520, 1493144580, 1493144640, 1493144700, 1493144760, 1493144820, 1493144880, 1493144940, 1493145000, 1493145060, 1493145120, 1493145180, 1493145240, 1493145300, 1493145360, 1493145420, 1493145480, 1493145540, 1493145600, 1493145660, 1493145720, 1493145780, 1493145840, 1493145900, 1493145960, 1493146020, 1493146080, 1493146140, 1493146200, 1493146260, 1493146320, 1493146380, 1493146440, 1493146500, 1493146560, 1493146620, 1493146680, 1493146740, 1493146800, 1493146860, 1493146920, 1493146980, 1493147040, 1493147100, 1493147160, 1493147220, 1493147280, 1493147340, 1493147400, 1493147460, 1493147520, 1493147580, 1493147640, 1493147700, 1493147760, 1493147820, 1493147880, 1493147940, 1493148000, 1493148060, 1493148120, 1493148180, 1493148240, 1493148300, 1493148360, 1493148420, 1493148480, 1493148540, 1493148600, 1493148660, 1493148720, 1493148780, 1493148840, 1493148900, 1493148960, 1493149020, 1493149080, 1493149140, 1493149200, 1493149260, 1493149320, 1493149380, 1493149440, 1493149500, 1493149560, 1493149620, 1493149680, 1493149740, 1493149800, 1493149860, 1493149920, 1493149980, 1493150040, 1493150100, 1493150160, 1493150220, 1493150280, 1493150340, 1493150400, 1493150460, 1493150520))
)
fit.xts <- auto.arima(qxts$y)
forecast_length <- 10
fore.xts <- forecast(fit.xts, h=forecast_length)
fore.dates <- seq(as.POSIXct(qxts$date[length(qxts$date)], origin='1970-01-01'), by=qxts$date[length(qxts$date)] - qxts$date[length(qxts$date)-1], len=forecast_length)
p <- plot_ly() %>%
add_lines(x = as.POSIXct(qxts$date, origin='1970-01-01'), y = qxts$y,
color = I("black"),
name = "observed",
marker=list(mode='lines')) %>%
add_lines(x = fore.dates, y = fore.xts$mean, color = I("blue"), name = "prediction") %>%
add_ribbons(x = fore.dates,
ymin = fore.xts$lower[, 2],
ymax = fore.xts$upper[, 2],
color = I("gray95"),
name = "95% confidence") %>%
add_ribbons(p,
x = fore.dates,
ymin = fore.xts$lower[, 1],
ymax = fore.xts$upper[, 1],
color = I("gray80"), name = "80% confidence")
p
我想使用 plotly 绘制时间序列的预测图。但我的时间序列索引由时间戳 [日期时间] 值组成。这使得很难绘制预测部分。
> qxts
[,1]
2017-04-25 16:52:00 -0.4120000
2017-04-25 16:53:00 -0.4526667
2017-04-25 16:54:00 -0.4586667
2017-04-25 16:55:00 -0.4606667
2017-04-25 16:56:00 -0.5053333
2017-04-25 16:57:00 -0.5066667
2017-04-25 16:58:00 -0.5100000
2017-04-25 16:59:00 -0.4986667
2017-04-25 17:00:00 -0.5026667
2017-04-25 17:01:00 -0.5053333
2017-04-25 17:02:00 -0.5020000
2017-04-25 17:03:00 -0.5066667
2017-04-25 17:04:00 -0.5080000
2017-04-25 17:05:00 -0.5040000
2017-04-25 17:06:00 -0.5013333
2017-04-25 17:07:00 -0.5020000
2017-04-25 17:08:00 -0.5020000
2017-04-25 17:09:00 -0.5040000
2017-04-25 17:10:00 -0.5046667
2017-04-25 17:11:00 -0.5053333
2017-04-25 17:12:00 -0.5013333
2017-04-25 17:13:00 -0.5033333
2017-04-25 17:14:00 -0.4966667
2017-04-25 17:15:00 -0.5040000
2017-04-25 17:16:00 -0.4980000
2017-04-25 17:17:00 -0.3893333
2017-04-25 17:18:00 -0.3640000
2017-04-25 17:19:00 -0.3653333
2017-04-25 17:20:00 -0.3586667
2017-04-25 17:21:00 -0.3600000
2017-04-25 17:22:00 -0.3553333
2017-04-25 17:23:00 -0.3653333
2017-04-25 17:24:00 -0.3606667
2017-04-25 17:25:00 -0.3640000
2017-04-25 17:26:00 -0.3546667
2017-04-25 17:27:00 -0.3700000
2017-04-25 17:28:00 -0.3653333
2017-04-25 17:29:00 -0.3673333
2017-04-25 17:30:00 -0.3966667
2017-04-25 17:31:00 -0.4693333
2017-04-25 17:32:00 -0.4586667
2017-04-25 17:33:00 -0.4640000
2017-04-25 17:34:00 -0.4573333
2017-04-25 17:35:00 -0.4646667
2017-04-25 17:36:00 -0.4680000
2017-04-25 17:37:00 -0.4688000
2017-04-25 17:38:00 -0.4673333
2017-04-25 17:39:00 -0.4606667
2017-04-25 17:40:00 -0.4653333
2017-04-25 17:41:00 -0.4720000
2017-04-25 17:42:00 -0.4533333
2017-04-25 17:43:00 -0.4660000
2017-04-25 17:44:00 -0.4646667
2017-04-25 17:45:00 -0.4593333
2017-04-25 17:46:00 -0.4646667
2017-04-25 17:47:00 -0.4580000
2017-04-25 17:48:00 -0.4646667
2017-04-25 17:49:00 -0.4740000
2017-04-25 17:50:00 -0.4173333
2017-04-25 17:51:00 -0.4640000
2017-04-25 17:52:00 -0.4600000
2017-04-25 17:53:00 -0.4686667
2017-04-25 17:54:00 -0.4720000
2017-04-25 17:55:00 -0.4653333
2017-04-25 17:56:00 -0.4720000
2017-04-25 17:57:00 -0.4626667
2017-04-25 17:58:00 -0.4624000
2017-04-25 17:59:00 -0.4653333
2017-04-25 18:00:00 -0.4673333
2017-04-25 18:01:00 -0.4600000
2017-04-25 18:02:00 -0.4646667
2017-04-25 18:03:00 -0.4613333
2017-04-25 18:04:00 -0.4660000
2017-04-25 18:05:00 -0.4620000
2017-04-25 18:06:00 -0.4620000
2017-04-25 18:07:00 -0.4666667
2017-04-25 18:08:00 -0.4573333
2017-04-25 18:09:00 -0.4660000
2017-04-25 18:10:00 -0.4640000
2017-04-25 18:11:00 -0.4640000
2017-04-25 18:12:00 -0.4886667
2017-04-25 18:13:00 -0.5433333
2017-04-25 18:14:00 -0.4893333
2017-04-25 18:15:00 -0.3600000
2017-04-25 18:16:00 0.0000000
2017-04-25 18:17:00 0.0000000
2017-04-25 18:18:00 0.0000000
2017-04-25 18:19:00 0.0000000
2017-04-25 18:20:00 0.0000000
2017-04-25 18:21:00 0.0000000
2017-04-25 18:22:00 0.0000000
2017-04-25 18:23:00 0.0000000
2017-04-25 18:24:00 0.0000000
2017-04-25 18:25:00 0.0000000
2017-04-25 18:26:00 0.0000000
2017-04-25 18:27:00 0.0000000
2017-04-25 18:28:00 0.0000000
2017-04-25 18:29:00 0.0000000
2017-04-25 18:30:00 0.0000000
2017-04-25 18:31:00 0.0000000
2017-04-25 18:32:00 0.0000000
2017-04-25 18:33:00 0.0000000
2017-04-25 18:34:00 0.0000000
2017-04-25 18:35:00 0.0000000
2017-04-25 18:36:00 -0.3573333
2017-04-25 18:37:00 -0.4646667
2017-04-25 18:38:00 -0.3360000
2017-04-25 18:39:00 -0.4453333
2017-04-25 18:40:00 -0.4933333
2017-04-25 18:41:00 -0.4446667
2017-04-25 18:42:00 -0.3253333
2017-04-25 18:43:00 -0.6020000
2017-04-25 18:44:00 -0.3304000
2017-04-25 18:45:00 -0.3946667
2017-04-25 18:46:00 -0.4066667
2017-04-25 18:47:00 -0.4066667
2017-04-25 18:48:00 -0.5253333
2017-04-25 18:49:00 -0.5520000
2017-04-25 18:50:00 -0.6973333
2017-04-25 18:51:00 -0.5260000
2017-04-25 18:52:00 -0.5653333
2017-04-25 18:53:00 -0.4514286
2017-04-25 18:54:00 -0.3973333
2017-04-25 18:55:00 -0.5920000
2017-04-25 18:56:00 -0.4912000
2017-04-25 18:57:00 -0.4313333
2017-04-25 18:58:00 -0.1973333
2017-04-25 18:59:00 -0.5460000
2017-04-25 19:00:00 -0.4960000
2017-04-25 19:01:00 -0.4595000
2017-04-25 19:02:00 -0.4592000
2017-04-25 19:03:00 -0.4537143
2017-04-25 19:04:00 -0.4553333
2017-04-25 19:05:00 -0.4613333
2017-04-25 19:06:00 -0.4620000
2017-04-25 19:07:00 -0.4573333
2017-04-25 19:08:00 -0.4606667
2017-04-25 19:09:00 -0.4560000
2017-04-25 19:10:00 -0.4593333
2017-04-25 19:11:00 -0.4580000
2017-04-25 19:12:00 -0.4568000
2017-04-25 19:13:00 -0.4554286
2017-04-25 19:14:00 -0.4546667
2017-04-25 19:15:00 -0.4540000
2017-04-25 19:16:00 -0.4526667
2017-04-25 19:17:00 -0.4640000
2017-04-25 19:18:00 -0.4566667
2017-04-25 19:19:00 -0.4544000
2017-04-25 19:20:00 -0.4566667
2017-04-25 19:21:00 -0.4546667
2017-04-25 19:22:00 -0.4600000
2017-04-25 19:23:00 -0.4584000
2017-04-25 19:24:00 -0.4593333
2017-04-25 19:25:00 -0.3946667
2017-04-25 19:26:00 -0.3673333
2017-04-25 19:27:00 -0.2824000
2017-04-25 19:28:00 -0.3446667
2017-04-25 19:29:00 -0.4646667
2017-04-25 19:30:00 -0.4433333
2017-04-25 19:31:00 -0.3392000
2017-04-25 19:32:00 -0.3326667
2017-04-25 19:33:00 -0.3633333
2017-04-25 19:34:00 -0.4360000
2017-04-25 19:35:00 -0.5180000
2017-04-25 19:36:00 -0.1593333
2017-04-25 19:37:00 -0.2493333
2017-04-25 19:38:00 -0.4336000
2017-04-25 19:39:00 -0.4626667
2017-04-25 19:40:00 -0.4100000
2017-04-25 19:41:00 -0.3573333
2017-04-25 19:42:00 -0.3616000
2017-04-25 19:43:00 -0.4946667
2017-04-25 19:44:00 -0.5220000
2017-04-25 19:45:00 -0.2940000
2017-04-25 19:46:00 -0.4528000
2017-04-25 19:47:00 -0.4740000
2017-04-25 19:48:00 -0.3426667
2017-04-25 19:49:00 -0.5368000
2017-04-25 19:50:00 -0.4233333
2017-04-25 19:51:00 -0.4986667
2017-04-25 19:52:00 -0.4393333
2017-04-25 19:53:00 -0.4648000
2017-04-25 19:54:00 -0.3686667
2017-04-25 19:55:00 -0.2673333
2017-04-25 19:56:00 -0.3033333
2017-04-25 19:57:00 -0.2128000
2017-04-25 19:58:00 -0.3980000
2017-04-25 19:59:00 -0.4446667
2017-04-25 20:00:00 -0.6368000
2017-04-25 20:01:00 -0.3373333
2017-04-25 20:02:00 -0.2600000
预测我的时间序列
fit.xts <- auto.arima(qxts)
fore.xts <- forecast(fit.xts, h=10)
绘制预测图
plot_ly() %>% add_lines(x = ~time(qxts), y = ~qxts,
color = I("black"), name = "observed") %>%
add_ribbons(x = time(fore.xts$mean), ymin = fore.xts$lower[, 2], ymax =
fore.xts$upper[, 2],
color = I("gray95"), name = "95% confidence") %>%
add_ribbons(x = time(fore.xts$mean), ymin = fore.xts$lower[, 1], ymax =
fore.xts$upper[, 1],color = I("gray80"), name = "80% confidence") %>%
add_lines(x = time(fore.xts$mean), y = fore.xts$mean, color = I("blue"), name = "prediction")
这给出了空图。有人能帮我吗？这两天一直在找答案
dput(qxts) structure(c(-0.4120000005, -0.452666665083333, -0.458666667316667, -0.460666666416667, -0.505333344133333, -0.506666670233333, -0.50999999045, -0.498666668933333, -0.502666672066667, -0.505333344133333, -0.50200000405, -0.506666680166667, -0.508000006266667, -0.5040000081, -0.501333341, -0.50200000405, -0.502000009016667, -0.504000003133333, -0.50466667115, -0.505333344133333, -0.501333341, -0.503333340083333, -0.496666664883333, -0.5040000081, -0.49799999595, -0.389333337533333, -0.3640000075, -0.365333338566667, -0.3586666733, -0.3599999994, -0.355333338183333, -0.365333338566667, -0.360666667416667, -0.3640000075, -0.3546666652, -0.369999999783333, -0.3653333336, -0.36733333765, -0.396666670833333, -0.469333335766667, -0.4586666673, -0.4639999916, -0.457333331283333, -0.4646666596, -0.4680000047, -0.4687999964, -0.467333336683333, -0.460666661433333, -0.4653333326, -0.4720000029, -0.453333328166667, -0.466000000616667, -0.4646666646, -0.45933333535, -0.464666659633333, -0.4579999993, -0.464666669566667, -0.473999996983333, -0.417333334666667, -0.46400000155, -0.4599999934, -0.46866666775, -0.47200000288, -0.465333337583333, -0.4719999979, -0.462666660516667, -0.46240000126, -0.465333322666667, -0.4673333317, -0.459999993416667, -0.464666659616667, -0.461333329466667, -0.46599999565, -0.461999997483333, -0.461999992516667, -0.466666663683333, -0.457333336283333, -0.46600000065, -0.463999996566667, -0.46400000155, -0.488666658566667, -0.543333326766667, -0.489333341533333, -0.3600000143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3573333323, -0.464666662133333, -0.3360000037, -0.445333333066667, -0.49333334465, -0.444666661316667, -0.325333339483333, -0.60199998815, -0.33039999454, -0.394666674216667, -0.406666671233333, -0.4066666588, -0.52533333005, -0.552000006066667, -0.6973333458, -0.526000003, -0.565333336583333, -0.451428575185714, -0.3973333314, -0.591999994871429, -0.49120000004, -0.4313333432, -0.197333325966667, -0.545999998833333, -0.49600000976, -0.4595000035875, -0.45920000072, -0.453714281285714, -0.455333337166667, -0.461333334433333, -0.461999992533333, -0.457333331283333, -0.4606666664, -0.456000005183333, -0.4593333254, -0.4579999993, -0.45679999586, -0.455428570485714, -0.454666664183333, -0.453999991183333, -0.452666665083333, -0.464000001533333, -0.456666668216667, -0.45439999696, -0.45666666825, -0.454666664166667, -0.459999998383333, -0.45840000508, -0.459333330366667, -0.394666661816667, -0.367333332983333, -0.2823999971, -0.3446666648, -0.464666659633333, -0.443333327766667, -0.33920000494, -0.332666660333333, -0.363333340716667, -0.43599999546, -0.518000001716667, -0.15933333585, -0.2493333357, -0.43359999356, -0.462666669166667, -0.410000008833333, -0.357333337883333, -0.3615999937, -0.494666665783333, -0.52200000485, -0.29400000225, -0.4527999997, -0.474000004416667, -0.342666663233333, -0.53680000306, -0.423333331933333, -0.498666671416667, -0.439333344516667, -0.46480000914, -0.368666668733333, -0.2673333399, -0.303333333366667, -0.21279999912, -0.398000004383333, -0.444666676216667, -0.63679999114, -0.337333336466667, -0.2599999905 ), .Dim = c(191L, 1L), index = structure(c(1493139120, 1493139180, 1493139240, 1493139300, 1493139360, 1493139420, 1493139480, 1493139540, 1493139600, 1493139660, 1493139720, 1493139780, 1493139840, 1493139900, 1493139960, 1493140020, 1493140080, 1493140140, 1493140200, 1493140260, 1493140320, 1493140380, 1493140440, 1493140500, 1493140560, 1493140620, 1493140680, 1493140740, 1493140800, 1493140860, 1493140920, 1493140980, 1493141040, 1493141100, 1493141160, 1493141220, 1493141280, 1493141340, 1493141400, 1493141460, 1493141520, 1493141580, 1493141640, 1493141700, 1493141760, 1493141820, 1493141880, 1493141940, 1493142000, 1493142060, 1493142120, 1493142180, 1493142240, 1493142300, 1493142360, 1493142420, 1493142480, 1493142540, 1493142600, 1493142660, 1493142720, 1493142780, 1493142840, 1493142900, 1493142960, 1493143020, 1493143080, 1493143140, 1493143200, 1493143260, 1493143320, 1493143380, 1493143440, 1493143500, 1493143560, 1493143620, 1493143680, 1493143740, 1493143800, 1493143860, 1493143920, 1493143980, 1493144040, 1493144100, 1493144160, 1493144220, 1493144280, 1493144340, 1493144400, 1493144460, 1493144520, 1493144580, 1493144640, 1493144700, 1493144760, 1493144820, 1493144880, 1493144940, 1493145000, 1493145060, 1493145120, 1493145180, 1493145240, 1493145300, 1493145360, 1493145420, 1493145480, 1493145540, 1493145600, 1493145660, 1493145720, 1493145780, 1493145840, 1493145900, 1493145960, 1493146020, 1493146080, 1493146140, 1493146200, 1493146260, 1493146320, 1493146380, 1493146440, 1493146500, 1493146560, 1493146620, 1493146680, 1493146740, 1493146800, 1493146860, 1493146920, 1493146980, 1493147040, 1493147100, 1493147160, 1493147220, 1493147280, 1493147340, 1493147400, 1493147460, 1493147520, 1493147580, 1493147640, 1493147700, 1493147760, 1493147820, 1493147880, 1493147940, 1493148000, 1493148060, 1493148120, 1493148180, 1493148240, 1493148300, 1493148360, 1493148420, 1493148480, 1493148540, 1493148600, 1493148660, 1493148720, 1493148780, 1493148840, 1493148900, 1493148960, 1493149020, 1493149080, 1493149140, 1493149200, 1493149260, 1493149320, 1493149380, 1493149440, 1493149500, 1493149560, 1493149620, 1493149680, 1493149740, 1493149800, 1493149860, 1493149920, 1493149980, 1493150040, 1493150100, 1493150160, 1493150220, 1493150280, 1493150340, 1493150400, 1493150460, 1493150520), tzone = "", tclass = c("POSIXct", "POSIXt")), class = c("xts", "zoo"), .indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct", "POSIXt"), .indexTZ = "", tzone = "")
您可以通过 as.POSIXct 转换您的时间戳，然后绘制它。对于预测，您还需要提供日期（在以下示例中，我假设时差始终相等）。
library(forecast)
qxts = structure(list(y=c(-0.4120000005, -0.452666665083333, -0.458666667316667, -0.460666666416667, -0.505333344133333, -0.506666670233333, -0.50999999045, -0.498666668933333, -0.502666672066667, -0.505333344133333, -0.50200000405, -0.506666680166667, -0.508000006266667, -0.5040000081, -0.501333341, -0.50200000405, -0.502000009016667, -0.504000003133333, -0.50466667115, -0.505333344133333, -0.501333341, -0.503333340083333, -0.496666664883333, -0.5040000081, -0.49799999595, -0.389333337533333, -0.3640000075, -0.365333338566667, -0.3586666733, -0.3599999994, -0.355333338183333, -0.365333338566667, -0.360666667416667, -0.3640000075, -0.3546666652, -0.369999999783333, -0.3653333336, -0.36733333765, -0.396666670833333, -0.469333335766667, -0.4586666673, -0.4639999916, -0.457333331283333, -0.4646666596, -0.4680000047, -0.4687999964, -0.467333336683333, -0.460666661433333, -0.4653333326, -0.4720000029, -0.453333328166667, -0.466000000616667, -0.4646666646, -0.45933333535, -0.464666659633333, -0.4579999993, -0.464666669566667, -0.473999996983333, -0.417333334666667, -0.46400000155, -0.4599999934, -0.46866666775, -0.47200000288, -0.465333337583333, -0.4719999979, -0.462666660516667, -0.46240000126, -0.465333322666667, -0.4673333317, -0.459999993416667, -0.464666659616667, -0.461333329466667, -0.46599999565, -0.461999997483333, -0.461999992516667, -0.466666663683333, -0.457333336283333, -0.46600000065, -0.463999996566667, -0.46400000155, -0.488666658566667, -0.543333326766667, -0.489333341533333, -0.3600000143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3573333323, -0.464666662133333, -0.3360000037, -0.445333333066667, -0.49333334465, -0.444666661316667, -0.325333339483333, -0.60199998815, -0.33039999454, -0.394666674216667, -0.406666671233333, -0.4066666588, -0.52533333005, -0.552000006066667, -0.6973333458, -0.526000003, -0.565333336583333, -0.451428575185714, -0.3973333314, -0.591999994871429, -0.49120000004, -0.4313333432, -0.197333325966667, -0.545999998833333, -0.49600000976, -0.4595000035875, -0.45920000072, -0.453714281285714, -0.455333337166667, -0.461333334433333, -0.461999992533333, -0.457333331283333, -0.4606666664, -0.456000005183333, -0.4593333254, -0.4579999993, -0.45679999586, -0.455428570485714, -0.454666664183333, -0.453999991183333, -0.452666665083333, -0.464000001533333, -0.456666668216667, -0.45439999696, -0.45666666825, -0.454666664166667, -0.459999998383333, -0.45840000508, -0.459333330366667, -0.394666661816667, -0.367333332983333, -0.2823999971, -0.3446666648, -0.464666659633333, -0.443333327766667, -0.33920000494, -0.332666660333333, -0.363333340716667, -0.43599999546, -0.518000001716667, -0.15933333585, -0.2493333357, -0.43359999356, -0.462666669166667, -0.410000008833333, -0.357333337883333, -0.3615999937, -0.494666665783333, -0.52200000485, -0.29400000225, -0.4527999997, -0.474000004416667, -0.342666663233333, -0.53680000306, -0.423333331933333, -0.498666671416667, -0.439333344516667, -0.46480000914, -0.368666668733333, -0.2673333399, -0.303333333366667, -0.21279999912, -0.398000004383333, -0.444666676216667, -0.63679999114, -0.337333336466667, -0.2599999905 ),
date = c(1493139120, 1493139180, 1493139240, 1493139300, 1493139360, 1493139420, 1493139480, 1493139540, 1493139600, 1493139660, 1493139720, 1493139780, 1493139840, 1493139900, 1493139960, 1493140020, 1493140080, 1493140140, 1493140200, 1493140260, 1493140320, 1493140380, 1493140440, 1493140500, 1493140560, 1493140620, 1493140680, 1493140740, 1493140800, 1493140860, 1493140920, 1493140980, 1493141040, 1493141100, 1493141160, 1493141220, 1493141280, 1493141340, 1493141400, 1493141460, 1493141520, 1493141580, 1493141640, 1493141700, 1493141760, 1493141820, 1493141880, 1493141940, 1493142000, 1493142060, 1493142120, 1493142180, 1493142240, 1493142300, 1493142360, 1493142420, 1493142480, 1493142540, 1493142600, 1493142660, 1493142720, 1493142780, 1493142840, 1493142900, 1493142960, 1493143020, 1493143080, 1493143140, 1493143200, 1493143260, 1493143320, 1493143380, 1493143440, 1493143500, 1493143560, 1493143620, 1493143680, 1493143740, 1493143800, 1493143860, 1493143920, 1493143980, 1493144040, 1493144100, 1493144160, 1493144220, 1493144280, 1493144340, 1493144400, 1493144460, 1493144520, 1493144580, 1493144640, 1493144700, 1493144760, 1493144820, 1493144880, 1493144940, 1493145000, 1493145060, 1493145120, 1493145180, 1493145240, 1493145300, 1493145360, 1493145420, 1493145480, 1493145540, 1493145600, 1493145660, 1493145720, 1493145780, 1493145840, 1493145900, 1493145960, 1493146020, 1493146080, 1493146140, 1493146200, 1493146260, 1493146320, 1493146380, 1493146440, 1493146500, 1493146560, 1493146620, 1493146680, 1493146740, 1493146800, 1493146860, 1493146920, 1493146980, 1493147040, 1493147100, 1493147160, 1493147220, 1493147280, 1493147340, 1493147400, 1493147460, 1493147520, 1493147580, 1493147640, 1493147700, 1493147760, 1493147820, 1493147880, 1493147940, 1493148000, 1493148060, 1493148120, 1493148180, 1493148240, 1493148300, 1493148360, 1493148420, 1493148480, 1493148540, 1493148600, 1493148660, 1493148720, 1493148780, 1493148840, 1493148900, 1493148960, 1493149020, 1493149080, 1493149140, 1493149200, 1493149260, 1493149320, 1493149380, 1493149440, 1493149500, 1493149560, 1493149620, 1493149680, 1493149740, 1493149800, 1493149860, 1493149920, 1493149980, 1493150040, 1493150100, 1493150160, 1493150220, 1493150280, 1493150340, 1493150400, 1493150460, 1493150520))
)
fit.xts <- auto.arima(qxts$y)
forecast_length <- 10
fore.xts <- forecast(fit.xts, h=forecast_length)
fore.dates <- seq(as.POSIXct(qxts$date[length(qxts$date)], origin='1970-01-01'), by=qxts$date[length(qxts$date)] - qxts$date[length(qxts$date)-1], len=forecast_length)
p <- plot_ly() %>%
add_lines(x = as.POSIXct(qxts$date, origin='1970-01-01'), y = qxts$y,
color = I("black"),
name = "observed",
marker=list(mode='lines')) %>%
add_lines(x = fore.dates, y = fore.xts$mean, color = I("blue"), name = "prediction") %>%
add_ribbons(x = fore.dates,
ymin = fore.xts$lower[, 2],
ymax = fore.xts$upper[, 2],
color = I("gray95"),
name = "95% confidence") %>%
add_ribbons(p,
x = fore.dates,
ymin = fore.xts$lower[, 1],
ymax = fore.xts$upper[, 1],
color = I("gray80"), name = "80% confidence")
p