是否有一种简单的方法可以将预测恢复为时间序列进行绘图? [英] Is there an easy way to revert a forecast back into a time series for plotting?

问题描述

我是R的新手，并且发现此站点非常有用，因此涵盖了我问题的下半部分(每个帖子一个问题).感谢您的提前帮助.

I am new to R and have found this site extremely helpful, so this covers the second half of my question (one issue per post). Thank you for your assistance ahead of time.

背景:我正在绘制历史数据，并叠加了多个预测以进行视觉准确性检查.当在观测"的x轴上显示时，此效果很好.但是，当在x轴上用日期绘制数据时，数据更容易理解，因此我使用ts()将其设置为时间序列，并按预期绘制了时间序列数据.但是，(A)由于它们不是时间序列，因此未按时标绘制预测数据； (B)我不确定如何将x轴强制加1年以允许显示预测.

Background: I was plotting historical data with multiple forecasts overlaid for visual accuracy checks. This worked great when displayed on an x axis of 'observations'. However, the data is more understandable when plotted with dates on the x axis, so I made it a time series using ts() and it plotted the time series data as expected. However, (A) it did not plot the forecast data on the time scale because they are not a time series; and (B) I was unsure how to force the x axis to plus 1 year to permit the forecast to display.

问题:(A)如何将原始时间戳恢复为预测数据?我知道我可以手动重新创建时间序列，但这在每次预测迭代中都需要.我已经考虑过使用Forecast()而不是dictate()，但是其他的预测迭代仍然存在一个不属于时间序列的问题. 是否有一种简单的方法可以将原始时间戳恢复为预测数据?

Question: (A) How do I restore the original time stamps to the forecast data? I know that I could manually recreate the time series, but this would be required in every iteration of the forecast. I have considered using forecast() instead of predict(), but the additional forecast iterations still have the same issue of not being a time series. Is there a simple way to restore the original time stamp to the forecast data?

  require(forecast)  [EDITED for clarity]

  data <- rep(cos(1:52*(3.1416/26)),5)*100+1000

  arima.ts <- ts(data,start=c(2009,1),frequency=52) #not plotted as time series

  # Create the current fit on data and predict one year out
  plot(arima.ts, type="l", xlab="weeks", ylab="counts",
  main="Overlay forecasts & actuals",
       sub="green=FIT(1-105,by 16) wks back & PREDICT(26) wks, blue=52 wks")
  ############## This plotted correctly as "Arima(data),..."
  arima.fit <- auto.arima(tail(arima.ts,156)) 
  arima.pred <- predict(arima.fit, n.ahead=52)
  lines(arima.pred$pred, col="blue")
  lines(arima.pred$pred+2*arima.pred$se, col="red")
  lines(arima.pred$pred-2*arima.pred$se, col="red")

  # Loop back and perform comparison plotting of forecast to actuals
  for (j in seq(1,105,by=16)) { 
    result <- tryCatch({
      ############## This plotted correctly as "Arima(head(data,-j),..."
      arima1.fit <- auto.arima(head(tail(arima.ts,-j),156))
      arima1.pred <- predict(arima1.fit, n.ahead=52)
      lines(arima1.pred$pred, col="green", lty=(numtests %% 6) + 1 )
    }, error = function(e) {return(e$message)}) ## Trap errors
  }

推荐答案

要解决的核心问题是如何将原始时间戳恢复为预测数据" .通过反复试验，我了解到的是配置，然后再通过应用以下步骤就不要丢失时间序列属性" :

The core question being addressed is "how to restore the original time stamps to the forecast data". What I have learned with trial and error is "configure, then never loose the time series attribute" by applying these steps:

1:创建时间序列.使用ts()命令并创建时间序列.
2:子集时间序列使用"window()"在"for()"循环中创建时间序列的子集.在数据上使用"start()"和"end()"以显示时间轴位置.
3:预测时间序列.使用按时间序列运行的"forecast()"或"predict()".
4:绘制时间序列:绘制时间序列时，时间轴将使用lines()命令正确对齐以获取其他数据. {绘图选项是用户偏好.}

1: Make a time series Use the ts() command and create a time series.
2: Subset a time series Use 'window()' to create a subset of the time series in 'for()' loop. Use 'start()' and 'end()' on the data to show the time axis positions.
3: Forecast a time series Use 'forecast()' or 'predict()' which operate on time series.
4: Plot a time series When you plot a time series, then the time axis will align correctly for additional data using the lines() command. {Plotting options are user preference.}

这将使预测在正确的时间轴位置上绘制在历史数据上.

This causes the forecasts to be plotted over the historical data in the correct time axis location.

  require(forecast)     ### [EDITED for clarity]

  data <- rep(cos(1:52*(3.1416/26)),5)*100+1000
  a.ts <- ts(data,start=c(2009,1),frequency=52)

  ## Predict from previous '3' years then one year out & generate the plot
  a.win  <- window(a.ts,start=c(end(a.ts)[1]-3,end(a.ts)[2]),frequency=52)
  a.fit  <- auto.arima(a.win)  
  a.pred <- forecast(a.fit, h=52)
  plot(a.pred, type="l", xlab="weeks", ylab="counts",
       main="Overlay forecasts & actuals",
       sub="green=FIT(1-105,by 16) wks back & PREDICT(26) wks, blue=52 wks")

  for (j in seq(1, 90, by=8)) {   ## Loop to overlay early forecasts 
    result1 <- tryCatch({
      b.end   <- c(end(a.ts)[1],end(a.ts)[2]-j) ## Window the time series  
      b.start <- c(b.end[1]-3,b.end[2])
      b.window <- window(a.ts, start=b.start, end=b.end, frequency=52)

      b.fit  <-auto.arima(b.window) 
      b.pred <- forecast(b.fit, h=26)
      lines(b.pred$mean, col="green", lty="dashed" )
    }, error = function(e) {return(e$message)} ) ## Skip Errors
  }

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