使用ggplot2和funggcast函数进行预测

时间：2015-01-30 21:29:35

标签： r ggplot2 forecasting

在this网站上，Davenport先生发布了一个函数，用ggplot2对任意数据集的例子绘制arima预测，他发表了here。我可以按照他的例子没有任何错误信息。

现在，当我使用我的数据时，我会以警告结束：

1: In window.default(x, ...) : 'end' value not changed
2: In window.default(x, ...) : 'end' value not changed

我知道当我调用此命令pd <- funggcast(yt, yfor)时会发生这种情况，因为我在数据end = c(2013)中指出的数据存在问题。但我不知道如何解决这个问题。

这是我使用的代码：

library(ggplot2)
library(zoo)
library(forecast)

myts <- ts(rnorm(55), start = c(1960), end = c(2013), freq = 1)
funggcast <- function(dn, fcast){

en <- max(time(fcast$mean)) # Extract the max date used in the forecast

# Extract Source and Training Data
ds <- as.data.frame(window(dn, end = en))
names(ds) <- 'observed'
ds$date <- as.Date(time(window(dn, end = en)))

# Extract the Fitted Values (need to figure out how to grab confidence intervals)
dfit <- as.data.frame(fcast$fitted)
dfit$date <- as.Date(time(fcast$fitted))
names(dfit)[1] <- 'fitted'

ds <- merge(ds, dfit, all.x = T) # Merge fitted values with source and training data

# Extract the Forecast values and confidence intervals
dfcastn <- as.data.frame(fcast)
dfcastn$date <- as.Date(as.yearmon(row.names(dfcastn)))
names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date')

pd <- merge(ds, dfcastn,all.x = T) # final data.frame for use in ggplot
return(pd)

} 

yt <- window(myts, end = c(2013)) # extract training data until last year
yfit <- auto.arima(myts) # fit arima model
yfor <- forecast(yfit) # forecast
pd <- funggcast(yt, yfor) # extract the data for ggplot using function funggcast()

ggplot(data = pd, aes(x = date,y = observed)) + geom_line(color = "red") + geom_line(aes(y = fitted), color = "blue") + geom_line(aes(y = forecast)) + geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25) + scale_x_date(name = "Time in Decades") + scale_y_continuous(name = "GDP per capita (current US$)") + theme(axis.text.x = element_text(size = 10), legend.justification=c(0,1), legend.position=c(0,1)) + ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)") + scale_color_manual(values = c("Blue", "Red"), breaks = c("Fitted", "Data", "Forecast"))

修改：我发现另一个博客here有一个与forecast和ggplot2一起使用的功能但我想使用上面的方法，如果我能够找到我的错误。任何人吗？

EDIT2： 如果我使用我的数据here运行您的更新代码，那么我将在下面显示图表。请注意，我没有更改end = c(2023)的{​​{1}}，否则它不会将预测值与拟合值合并。

mtys

我得到的几乎完美的图表： enter image description here

另外一个问题：如何在此图表中获得图例？

如果我为myts <- ts(WDI_gdp_capita$Brazil, start = c(1960), end = c(2023), freq = 1) funggcast <- function(dn, fcast){ en <- max(time(fcast$mean)) # Extract the max date used in the forecast # Extract Source and Training Data ds <- as.data.frame(window(dn, end = en)) names(ds) <- 'observed' ds$date <- as.Date(time(window(dn, end = en))) # Extract the Fitted Values (need to figure out how to grab confidence intervals) dfit <- as.data.frame(fcast$fitted) dfit$date <- as.Date(time(fcast$fitted)) names(dfit)[1] <- 'fitted' ds <- merge(ds, dfit, all = T) # Merge fitted values with source and training data # Extract the Forecast values and confidence intervals dfcastn <- as.data.frame(fcast) dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-")) names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date') pd <- merge(ds, dfcastn,all.x = T) # final data.frame for use in ggplot return(pd) } # ggplot function by Frank Davenport yt <- window(myts, end = c(2013)) # extract training data until last year yfit <- auto.arima(yt) # fit arima model yfor <- forecast(yfit) # forecast pd <- funggcast(myts, yfor) # extract the data for ggplot using function funggcast() ggplot(data = pd, aes(x = date, y = observed)) + geom_line(color = "red") + geom_line(aes(y = fitted), color = "blue") + geom_line(aes(y = forecast)) + geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25) + scale_x_date(name = "Time in Decades") + scale_y_continuous(name = "GDP per capita (current US$)") + theme(axis.text.x = element_text(size = 10), legend.justification=c(0,1), legend.position=c(0,1)) + ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)") + scale_color_manual(values = c("Blue", "Red"), breaks = c("Fitted", "Data", "Forecast")) + ggsave((filename = "gdp_forecast_ggplot.pdf"), width=330, height=180, units=c("mm"), dpi = 300, limitsize = TRUE) 设置end = c(2013)，我会得到与开头相同的图表：

enter image description here

3 个答案:

答案 0 :(得分：5)

达文波特先生的分析与你想要制作的情节有几点不同。 第一个是他将arima预测与一些观测数据进行比较，这就是为什么他在整个时间序列的一部分训练集训练模型的原因。 为此，您应该使您的初始时间序列更长：

myts <- ts(rnorm(55), start = c(1960), end = c(2023), freq = 1)

然后在脚本的最后，选择2013年的培训：

yt <- window(myts, end = c(2013)) # extract training data until last year

模型应该在训练集上训练，而不是整个时间序列，所以你应该将yfit线改为：

yfit <- auto.arima(yt) # fit arima model

使用整个时间序列调用funggcast函数，因为它需要观察和拟合的数据：

pd <- funggcast(myts, yfor)

最后，他使用具有月份和年份的日期，因此在他的funggcast函数中，更改此行：

dfcastn$date <- as.Date(as.yearmon(row.names(dfcastn)))

要：

dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-"))

这是因为模型预测的值需要更改为日期，例如2014年必须更改为2014-01-01，以便与观察到的数据合并。

完成所有更改后，代码如下所示：

library(ggplot2)
library(zoo)
library(forecast)

myts <- ts(rnorm(55), start = c(1960), end = c(2013), freq = 1)
funggcast <- function(dn, fcast){

        en <- max(time(fcast$mean)) # Extract the max date used in the forecast

        # Extract Source and Training Data
        ds <- as.data.frame(window(dn, end = en))
        names(ds) <- 'observed'
        ds$date <- as.Date(time(window(dn, end = en)))

        # Extract the Fitted Values (need to figure out how to grab confidence intervals)
        dfit <- as.data.frame(fcast$fitted)
        dfit$date <- as.Date(time(fcast$fitted))
        names(dfit)[1] <- 'fitted'

        ds <- merge(ds, dfit, all.x = T) # Merge fitted values with source and training data

        # Extract the Forecast values and confidence intervals
        dfcastn <- as.data.frame(fcast)
        dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-"))
        names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date')

        pd <- merge(ds, dfcastn,all= T) # final data.frame for use in ggplot
        return(pd)

} 

yt <- window(myts, end = c(2013)) # extract training data until last year
yfit <- auto.arima(yt) # fit arima model
yfor <- forecast(yfit) # forecast
pd <- funggcast(myts, yfor) # extract the data for ggplot using function funggcast()

plotData<-ggplot(data = pd, aes(x = date, y = observed)) + geom_line(aes(color = "1")) +
        geom_line(aes(y = fitted,color="2")) + 
        geom_line(aes(y = forecast,color="3")) +
        scale_colour_manual(values=c("red", "blue","black"),labels = c("Observed", "Fitted", "Forecasted"),name="Data")+
        geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25)+
        scale_x_date(name = "Time in Decades") +
        scale_y_continuous(name = "GDP per capita (current US$)")+
        theme(axis.text.x = element_text(size = 10)) + 
        ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)")

plotData

你得到一个看起来像这样的情节，拟合非常糟糕，完全随机的时间序列。此外ggplot将输出一些错误，因为预测线在2013年之前没有数据，并且拟合的数据在2013年之后不会继续。（我运行了几次，取决于初始的随机时间序列，模型可能只是预测0到处都是）

enter image description here

编辑：如果2013年后没有观察到的数据，也会更改pd分配线

Edit2：我更改了代码末尾的ggplot函数，以确保图例显示

答案 1 :(得分：2)

github提供了一个名为ggfortify的软件包，允许使用ggplot2直接绘制预测对象。它可以在http://rpubs.com/sinhrks/plot_ts

上找到

答案 2 :(得分：0)

这是一个相当古老的帖子，但有一个fuction in github会产生一些不错的结果。

以下是2016年8月3日的代码：

function(forec.obj, data.color = 'blue', fit.color = 'red', forec.color = 'black',
                           lower.fill = 'darkgrey', upper.fill = 'grey', format.date = F)
{
    serie.orig = forec.obj$x
    serie.fit = forec.obj$fitted
    pi.strings = paste(forec.obj$level, '%', sep = '')

     if(format.date)
        dates = as.Date(time(serie.orig))
    else
        dates = time(serie.orig)

    serie.df = data.frame(date = dates, serie.orig = serie.orig, serie.fit = serie.fit)

    forec.M = cbind(forec.obj$mean, forec.obj$lower[, 1:2], forec.obj$upper[, 1:2])
    forec.df = as.data.frame(forec.M)
    colnames(forec.df) = c('forec.val', 'l0', 'l1', 'u0', 'u1')

    if(format.date)
        forec.df$date = as.Date(time(forec.obj$mean))
    else
        forec.df$date = time(forec.obj$mean)

    p = ggplot() + 
        geom_line(aes(date, serie.orig, colour = 'data'), data = serie.df) + 
        geom_line(aes(date, serie.fit, colour = 'fit'), data = serie.df) + 
        scale_y_continuous() +
        geom_ribbon(aes(x = date, ymin = l0, ymax = u0, fill = 'lower'), data = forec.df, alpha = I(0.4)) + 
        geom_ribbon(aes(x = date, ymin = l1, ymax = u1, fill = 'upper'), data = forec.df, alpha = I(0.3)) + 
        geom_line(aes(date, forec.val, colour = 'forecast'), data = forec.df) + 
        scale_color_manual('Series', values=c('data' = data.color, 'fit' = fit.color, 'forecast' = forec.color)) + 
        scale_fill_manual('P.I.', values=c('lower' = lower.fill, 'upper' = upper.fill))

    if (format.date)
        p = p + scale_x_date()

    p
}
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