我是2个月大的预测概念，但我正在努力学习并继续练习。在这里，我试图预测每周产品 使用不同的预测技术对训练数据集进行移动并测试其在测试数据集上的准确性。我尝试了不同的技术，如ARIMA，TBATS，Holts Winter，UCM，贝叶斯结构时间序列等。但是无法提高我的准确性。准确性似乎非常糟糕。不知道我哪里错了。我还尝试了ARIMA和回归量，但它再次对我没有帮助。我不确定我的代码或我的方法是否错误。任何人都可以指导我提高我的准确性吗？以下是每周数据集（从2012年12月8日开始）
[1] 74 76 78 63 58 58 57 56 85 73 71 91 85
[14] 79 101 74 86 98 131 90 127 116 320 145 121 148
[27] 112 141 153 118 151 151 152 90 147 123 266 99 110
[40] 146 134 76 81 100 80 323 15 22 14 13 19 56
[53] 78 79 70 79 24 26 31 35 45 33 41 41 61
[66] 91 83 76 57 68 87 82 105 76 107 116 105 124
[79] 127 149 124 120 111 122 134 87 80 81 89 40 63
[92] 112 85 131 97 51 65 74 70 47 62 60 49 47
[105] 56 64 57 58 45 56 60 49 82 49 61 71 61
[118] 92 90 75 69 114 79 144 121 133 132 114 124 152
[131] 125 112 128 124 152 95 64 59 91 132 146 120 196
[144] 212 115 125 66 68 78 83 74 300 46 98 86 95
[157] 61 73 89 56 81 60 58 101 482 55 124 72 57
[170] 51 82 55 68 105 153 113 105 85 34 77 95 96
[183] 97 94 81 104 76 97 65 42 18 11
我认为我的训练期为178周，测试为14周。 可以说，＆＃39;数据＆＃39;是我的数据框＆＃34;单位＆＃34;作为我的名字，
series <- ts(data, start=2012+342/365.25, frequency = 365.25/7)
kk <- 178
seas <- 365.25/7
st <- tsp(series)[1] + (1/seas)*(kk-1)
training <- window(series, end = st)
testing <- window(series, start = st + 1/52.17857, end = st+14/52.17857)
train1 <- training[,"units"]
test1 <- testing[,"units"]
##ARIMA
farima <- forecast(auto.arima(train1),h=14)
acc_arima <- accuracy(farima$mean,test1)
##TBATS
fTBATS <- forecast(tbats(train1,seasonal.periods=c(4,7,12,52)), h=14)
acc_TBATS <- accuracy(fTBATS$mean,test1)
##struTs
fstruTs <- forecast(StructTS(train1), h=14)
acc_struTs <- accuracy(fstruTs$mean,test1)
##UCM
forUCM <- ucm(formula = train1~0, data = train1, level =
TRUE, slope = TRUE)
fUCM <- predict(forUCM$model, n.ahead = 14)
acc_struTs <- accuracy(fUCM$fit,test1)
##Bayesian Structural time series
ss <- AddLocalLinearTrend(list(), train1)
ss <- AddSeasonal(ss, train1, nseasons = 52, season.duration = 7)
model2 <- bsts(train1, state.specification = ss, niter = 500)
fbsts <- predict(model2, horizon = 14, burn = 100)
acc_bsts <- accuracy(fbsts$mean,test1)
对于上述所有方法，我的MAPE高于100％，我认为非常糟糕。有人可以指导我提高准确性吗？我将非常感激。 谢谢！
答案 0 :(得分：1)
I would recommend a few things:
1) If you are using the excellent R forecast package, I would recommend at least trying the fully automated forecast (see examples below).
2) I would recommend plotting the forecast and actual values, along with the historic data to see if the output seems reasonable given the historic data.
3) I would recommend reading the free on-line textbook made by some of the creators of the R forecast package.
The example below uses the fully automated time series forecast from the forecast package and plots the results, both for the data-set you're using above, and another publicly available data-set.
library(ggplot2)
library(forecast)
data <- read.table("./data.txt", quote="\"", comment.char="")
series <- ts(as.numeric(data), start=2012+342/365.25, frequency = 365.25/7)
train_length <- 178
test_length <- length(series) - train_length
train_end <- time(series)[train_length]
test_start <- time(series)[train_length+1]
training <- window(series, end = train_end)
testing <- window(series, start = test_start)
## Use default forecast
fcast <- forecast(training, h=test_length)
plot(fcast)
lines(testing, col='red')
acc_fcast <- accuracy(fcast$mean, testing)
births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")
birthstimeseries <- ts(births, frequency=12, start=c(1946,1))
train_length <- 150
test_length <- length(birthstimeseries) - train_length
train_end <- time(birthstimeseries)[train_length]
test_start <- time(birthstimeseries)[train_length+1]
training <- window(birthstimeseries, end = train_end)
testing <- window(birthstimeseries, start = test_start)
## Use default forecast
fcast <- forecast(training, h=test_length)
plot(fcast)
lines(testing, col='red')
acc_births <- accuracy(fcast$mean, testing)