How to plot forecast using plotly for time series data having 'Index' with Datetime value
Question
I want to plot forecast of a time series using plotly. But my index of the timeseries consists of timestamp [Date Time] Values. which makes difficult to plot the forecast part.
> 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
forecasting my time series
fit.xts <- auto.arima(qxts)
fore.xts <- forecast(fit.xts, h=10)
plotting the forecasting
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")
This gives empty plot. Can someone help me? I have been looking for answers for two days.
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, -0.54599999 8833333, -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, -0.368666 668733333, -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, 1 493143140, 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, 14931 48120, 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 = "")
1 answers
solution1
0 ACCPTED 2017-04-29 18:23:55
You could convert your timestamps via as.POSIXct and then plot it. For the forecasts you would also need to supply the dates (in the following example I assumed that the time difference is always equal).
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
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.