我一直在使用R中的预测包，但发现很难将我自己的每日时间序列加载到ts对象中，然后将其与预测算法一起使用。我已经使用动物园来创建我的每日时间序列对象，但我无法将其直接传递给R Forecast包中的预测算法。
非常感谢任何正确方向的帮助。我觉得我很困惑。
由于
您好以下是一些示例代码。使用简单的季度数据集，我可以得到一些预测工作但是使用每日数据集我无法使其正常工作。非常感谢
require("forecast")
require("fpp")
# Example from Forecasting Principles and Practice
# http://otexts.com/fpp/2/5/
#beer2 <- window(ausbeer,start=1992,end=2006-.1)
#start with a really small dataset (only 6 data points)
beer2 <- window(ausbeer,start=2006,end=2006-.1)
print(beer2)
beerfit1 <- meanf(beer2,h=11)
beerfit2 <- rwf(beer2,h=11)
beerfit3 <- snaive(beer2,h=11)
plot(beerfit1, plot.conf=FALSE, main="Forecasts for quarterly beer production")
lines(beerfit2$mean,col=2)
lines(beerfit3$mean,col=3)
lines(ausbeer)
legend("topright", lty=1, col=c(4,2,3), legend=c("Mean method","Naive method","Seasonal naive method"))
beer3 <- window(ausbeer, start=2006)
accuracy(beerfit1, beer3)
accuracy(beerfit2, beer3)
accuracy(beerfit3, beer3)
#now make a really small daily dataset (Each day for two weeks)
forecast_datesequence = seq(from=as.Date("2013-05-06"), to=as.Date("2013-05-19"), by=1)
vals <- c(100,150,300,150,100,45,25,100,150,300,150,100,45,25)
dailyzoo_ts <- zoo(vals, forecast_datesequence)
print(daily_ts)
dailyfit1 <- meanf(coredata(dailyzoo_ts),h=7)
dailyfit2 <- rwf(coredata(dailyzoo_ts),h=7)
dailyfit3 <- snaive(coredata(dailyzoo_ts),h=7)
plot(dailyfit1, plot.conf=FALSE, main="Daily Data Over 2 Week Period")
lines(dailyfit2$mean,col=2)
lines(dailyfit3$mean,col=3)
lines(dailyzoo_ts)
legend("topright", lty=1, col=c(4,2,3), legend=c("Mean method","Naive method","Seasonal naive method"))
这是R代码的更新位仍无法正常工作
#now make a really small daily dataset (Each day for two weeks)
forecast_datesequence = seq(from=as.Date("2013-05-06"), to=as.Date("2013-05-19"), by=1)
vals <- c(100,150,300,150,100,45,25,100,150,300,150,100,45,25)
dailyzoo_ts <- zoo(vals, forecast_datesequence)
print(daily_ts)
z <- zoo(coredata(dailyzoo_ts), 1:14/7)
print(z)
plot(forecast(z))
#stl(z)
dailyfit1 <- meanf(z,h=7)
dailyfit2 <- rwf(z,h=7)
dailyfit3 <- snaive(z,h=7)
plot(dailyfit1, plot.conf=FALSE, main="Daily Data Over 2 Week Period")
lines(dailyfit2$mean,col=2)
lines(dailyfit3$mean,col=3)
lines(z)
legend("topright", lty=1, col=c(4,2,3), legend=c("Mean method","Naive method","Seasonal naive method"))
非常感谢
答案 0 :(得分：1)
forecast要求期间为1个单位，请尝试以下方法：
> z <- zoo(coredata(dailyzoo_ts), 0:15/7)
> plot(forecast(z))
> meanf(z)
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
1 124.375 11.38063 237.3694 -55.27649 304.0265
2 124.375 11.38063 237.3694 -55.27649 304.0265
3 124.375 11.38063 237.3694 -55.27649 304.0265
4 124.375 11.38063 237.3694 -55.27649 304.0265
5 124.375 11.38063 237.3694 -55.27649 304.0265
6 124.375 11.38063 237.3694 -55.27649 304.0265
7 124.375 11.38063 237.3694 -55.27649 304.0265
8 124.375 11.38063 237.3694 -55.27649 304.0265
9 124.375 11.38063 237.3694 -55.27649 304.0265
10 124.375 11.38063 237.3694 -55.27649 304.0265
> rwf(z)
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
16.14286 150 31.13735 268.8627 -31.78474 331.7847
16.28571 150 -18.09718 318.0972 -107.08245 407.0824
16.42857 150 -55.87615 355.8762 -164.86041 464.8604
16.57143 150 -87.72531 387.7253 -213.56948 513.5695
16.71429 150 -115.78497 415.7850 -256.48304 556.4830
16.85714 150 -141.15285 441.1529 -295.27986 595.2799
17.00000 150 -164.48102 464.4810 -330.95722 630.9572
17.14286 150 -186.19435 486.1944 -364.16489 664.1649
17.28571 150 -206.58796 506.5880 -395.35422 695.3542
17.42857 150 -225.87671 525.8767 -424.85383 724.8538
> snaive(z)
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
2.285714 300 300 300 300 300
2.428571 150 150 150 150 150
2.571429 100 100 100 100 100
2.714286 45 45 45 45 45
2.857143 25 25 25 25 25
3.000000 100 100 100 100 100
3.142857 150 150 150 150 150
3.285714 300 300 300 300 300
3.428571 150 150 150 150 150
3.571429 100 100 100 100 100
3.714286 45 45 45 45 45
3.857143 25 25 25 25 25
4.000000 100 100 100 100 100
4.142857 150 150 150 150 150
> stl(z, "periodic")
Call:
stl(x = z, s.window = "periodic")
Components
Time Series:
Start = c(0, 1)
End = c(2, 2)
Frequency = 7
seasonal trend remainder
0.0000000 -24.28571 124.2857 4.263256e-14
0.1428571 25.71429 124.2857 -2.842171e-14
0.2857143 175.71429 124.2857 -1.421085e-14
0.4285714 25.71429 124.2857 -1.421085e-14
0.5714286 -24.28571 124.2857 -1.421085e-14
0.7142857 -79.28571 124.2857 -1.421085e-14
0.8571429 -99.28571 124.2857 1.421085e-14
1.0000000 -24.28571 124.2857 4.263256e-14
1.1428571 25.71429 124.2857 -1.421085e-14
1.2857143 175.71429 124.2857 0.000000e+00
1.4285714 25.71429 124.2857 0.000000e+00
1.5714286 -24.28571 124.2857 1.421085e-14
1.7142857 -79.28571 124.2857 2.842171e-14
1.8571429 -99.28571 124.2857 -1.421085e-14
2.0000000 -24.28571 124.2857 -7.105427e-14
2.1428571 25.71429 124.2857 8.526513e-14