CODEWITHSUNDEEP
rfor-loopdplyrforecasting
silent_hunter
silent_hunter

Reputation: 2508

Extract forecasting data

I have one data frame, which is composed of three columns(AJT,NET and SAT). My intention is to make forecast with each of this three times series with forecast package. For that reason I convert data frame into ts object and make forecasting with snaive function,so I wrote this lines of code:

#CODE
      library(forecast)

# Data set
      DATA_SET<-data.frame(
        AJT=seq(1, 48, by = 2),
        NET=seq(1, 24, by = 1),
        SAT=seq(1, 94, by = 4)
        )

      # Making TS object
      TS_SALES<-ts(DATA_SET,start=c(2016,1),frequency = 12)

# Making forecasting with Forecast package      
      SNAIVE_AJT<-snaive(TS_SALES[, 'AJT'],h=5) 
      SNAIVE_NET<-snaive(TS_SALES[, 'NET'],h=5) 
      SNAIVE_SAT<-snaive(TS_SALES[, 'SAT'],h=5) 

# Union forecast in list
      SNAIVE_UNION<-mapply(SNAIVE_AJT, SNAIVE_NET,SNAIVE_SAT, FUN=list, SIMPLIFY=FALSE)

All outputs from snaive function I put into SNAIVE_UNION which contain all results from forecasting. So here most important component is "mean" which contain results of forecatig by months.

SNAIVE_UNION[["mean"]]
  # 
  # # [[1]]
  # # Jan Feb Mar Apr May
  # # 2018  25  27  29  31  33
  # # 
  # # [[2]]
  # # Jan Feb Mar Apr May
  # # 2018  13  14  15  16  17
  # # 
  # # [[3]]
  # # Jan Feb Mar Apr May
  # # 2018  49  53  57  61  65

So here, my intention is to put results form SNAIVE_UNION[["mean"]] ,into data table like table below with some function loop,for or other function

        Jan Feb Mar Apr May
------------------------------
AJT     25  27  29  31  33
NET     13  14  15  16  17
SAT     49  53  57  61  65

I am asking this because this time series is only small part of whole series and I would like to automate this code.

Upvotes: 0

Views: 62

Answers (1)

Marco Sandri
Marco Sandri

Reputation: 24252

You can use:

pred <- do.call(rbind, SNAIVE_UNION[["mean"]])

#      [,1] [,2] [,3] [,4] [,5]
# [1,]   25   27   29   31   33
# [2,]   13   14   15   16   17
# [3,]   49   53   57   61   65

Upvotes: 2

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