使用dplyr和do进行多步骤预测

Question

dplyr中的do-function让你可以快速轻松地制作很多很酷的模型,但我很难将这些模型用于良好的滚动预测.

# Data illustration

require(dplyr)
require(forecast)

df <- data.frame(
  Date = seq.POSIXt(from = as.POSIXct("2015-01-01 00:00:00"), 
                    to = as.POSIXct("2015-06-30 00:00:00"), by = "hour"))

  df <- df %>% mutate(Hour = as.numeric(format(Date, "%H")) + 1, 
                      Wind = runif(4320, min = 1, max = 5000), 
                      Temp = runif(4320, min = - 20, max = 25), 
                      Price = runif(4320, min = -15, max = 45)
                      )

我的因子变量是Hour,我的外生变量是Wind和temp,我想要预测的是Price.所以,基本上,我有24个模型,我希望能够进行滚动预测.

现在,我的数据框包含180天.我想回到100天,做一天滚动预测,然后能够将其与实际进行比较Price.

这种暴力行为看起来像这样:

# First I fit the data frame to be exactly the right length
# 100 days to start with (2015-03-21 or so), then 99, then 98.., etc. 
n <- 100 * 24

# Make the price <- NA so I can replace it with a forecast
df$Price[(nrow(df) - n): (nrow(df) - n + 24)] <- NA

# Now I make df just 81 days long, the estimation period + the first forecast
df <- df[1 : (nrow(df) - n + 24), ]

# The actual do & fit, later termed fx(df)

result <- df %>% group_by(Hour) %>% do ({
  historical <- .[!is.na(.$Price), ]
  forecasted <- .[is.na(.$Price), c("Date", "Hour", "Wind", "Temp")]
  fit <- Arima(historical$Price, xreg = historical[, 3:4], order = c(1, 1, 0))
  data.frame(forecasted[], 
             Price = forecast.Arima(fit, xreg = forecasted[3:4])$mean )
})

result

现在我将n改为99*24.但是将它放在循环或应用中会很棒,但我根本无法弄清楚如何做到这一点,并且还保存每个新的预测.

我尝试过这样的循环,但还没有运气:

# 100 days ago, forecast that day, then the next, etc.
for (n in 1:100) { 
  nx <- n * 24 * 80         # Because I want to start after 80 days
  df[nx:(nx + 23), 5] <- NA # Set prices to NA so I can forecast them
  fx(df) # do the function
  df.results[n] <- # Write the results into a vector / data frame to save them
    # and now rinse and repeat for n + 1
  }

真正令人敬畏的奖励积分为类似broom的解决方案:)

Answer 1

我首先会注意到 for 循环中存在错误。而不是n*24*80你的意思可能是(n+80)*24。如果您还想包含第 81 天的预测，则循环中的计数器也应该从 0 到 99，而不是 1 到 100。

我将尝试为您的问题提供一个优雅的解决方案。首先，我们以与您在帖子中所做的完全相同的方式定义测试数据框：

set.seed(2)
df <- data.frame(
Date = seq.POSIXt(from = as.POSIXct("2015-01-01 00:00:00"), 
                    to = as.POSIXct("2015-06-30 00:00:00"), by = "hour"))
df <- df %>% mutate(Hour = as.numeric(format(Date, "%H")) + 1, 
                    Wind = runif(4320, min = 1, max = 5000), 
                    Temp = runif(4320, min = - 20, max = 25), 
                    Price = runif(4320, min = -15, max = 45)
)

接下来，我们定义一个函数来执行某一特定日期的预测。输入参数是正在考虑的数据帧以及训练集中应包含的最小训练天数（在本示例中= 80）。minTrainingDays+offSet+1代表我们预测的实际日期。请注意，我们从 0 开始计算偏移量。

forecastOneDay <- function(theData,minTrainingDays,offset)
{
  nrTrainingRows <- (minTrainingDays+offset)*24

  theForecast <- theData %>% 
    filter(min_rank(Date) <= nrTrainingRows+24) %>% # Drop future data that we don't need
    group_by(Hour) %>%
    do ({
      trainingData <- head(.,-1) # For each group, drop the last entry from the dataframe
      forecastData <- tail(.,1) %>% select(Date,Hour,Wind,Temp) # For each group, predict the last entry
      fit <- Arima(trainingData$Price, xreg=trainingData[,3:4], order=c(1,1,0))
      data.frame(forecastData, realPrice = tail(.,1)$Price, predictedPrice = forecast.Arima(fit,xreg=forecastData[3:4])$mean)
    })
}

我们想要预测第 81-180 天。换句话说，我们的训练集至少需要 80 天，并且想要计算 offsets 的函数结果0:99。这可以通过简单的调用来完成lapply。最后，我们将所有结果合并到一个数据框中：

# Perform one day forecasts for days 81-180
resultList <- lapply(0:99, function(x) forecastOneDay(df,80,x))
# Merge all the results
mergedForecasts <- do.call("rbind",resultList)

编辑 在查看您的帖子和同时发布的另一个答案后，我注意到我的答案有两个潜在问题。首先，你想要一个滚动的80 天训练数据的然而，在我之前的代码中，所有可用的训练数据都用于拟合模型，而不是仅回溯 80 天。其次，该代码对 DST 更改的鲁棒性不强。

这两个问题已在下面的代码中修复。该函数的输入现在也更加直观：训练天数和实际预测天数可以用作输入参数。请注意，POSIXlt在对日期执行操作时，数据格式可以正确处理 DST、闰年等内容。因为数据框中的日期是类型的，所以POSIXct我们需要来回进行小的类型转换，以便正确处理事情。

新代码如下：

forecastOneDay <- function(theData,nrTrainingDays,predictDay) # predictDay should be greater than nrTrainingDays
{
  initialDate <- as.POSIXlt(theData$Date[1]); # First day (midnight hour)
  startDate <- initialDate # Beginning of training interval
  endDate <- initialDate # End of test interval

  startDate$mday <- initialDate$mday + (predictDay-nrTrainingDays-1) # Go back 80 days from predictday
  endDate$mday <- startDate$mday + (nrTrainingDays+1) # +1 to include prediction day

  theForecast <- theData %>% 
    filter(Date >= as.POSIXct(startDate),Date < as.POSIXct(endDate)) %>% 
    group_by(Hour) %>%
    do ({
      trainingData <- head(.,-1) # For each group, drop the last entry from the dataframe
      forecastData <- tail(.,1) %>% select(Date,Hour,Wind,Temp) # For each group, predict the last entry
      fit <- Arima(trainingData$Price, xreg=trainingData[,3:4], order=c(1,1,0))
      data.frame(forecastData, realPrice = tail(.,1)$Price, predictedPrice = forecast.Arima(fit,xreg=forecastData[3:4])$mean)
    })
}

# Perform one day forecasts for days 81-180
resultList <- lapply(81:180, function(x) forecastOneDay(df,80,x))
# Merge all the results
mergedForecasts <- do.call("rbind",resultList)

结果如下：

> head(mergedForecasts)
Source: local data frame [6 x 6]
Groups: Hour

                 Date Hour     Wind      Temp  realPrice predictedPrice
1 2015-03-22 00:00:00    1 1691.589 -8.722152 -11.207139       5.918541
2 2015-03-22 01:00:00    2 1790.928 18.098358   3.902686      37.885532
3 2015-03-22 02:00:00    3 1457.195 10.166422  22.193270      34.984164
4 2015-03-22 03:00:00    4 1414.502  4.993783   6.370435      12.037642
5 2015-03-22 04:00:00    5 3020.755  9.540715  25.440357      -1.030102
6 2015-03-22 05:00:00    6 4102.651  2.446729  33.528199      39.607848
> tail(mergedForecasts)
Source: local data frame [6 x 6]
Groups: Hour

                 Date Hour      Wind       Temp  realPrice predictedPrice
1 2015-06-29 18:00:00   19 1521.9609 13.6414797  12.884175     -6.7789109
2 2015-06-29 19:00:00   20  555.1534  3.4758159  37.958768     -5.1193514
3 2015-06-29 20:00:00   21 4337.6605  4.7242352  -9.244882     33.6817379
4 2015-06-29 21:00:00   22 3140.1531  0.8127839  15.825230     -0.4625457
5 2015-06-29 22:00:00   23 1389.0330 20.4667234 -14.802268     15.6755880
6 2015-06-29 23:00:00   24  763.0704  9.1646139  23.407525      3.8214642