R:splitting the dataset into groups for forecast - r

Question

i have dataset and i have to perform daily forecast splited by groups.
The group is client+stuff
ts <- read.csv("C:/Users/Admin/Desktop/mydat.csv",sep=";", dec=",")
here mydat
structure(list(Data = structure(c(1L, 3L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 2L, 4L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L), .Label = c("01.04.2017",
"01.06.2017", "02.04.2017", "02.06.2017", "03.04.2017", "04.04.2017",
"05.04.2017", "06.04.2017", "07.04.2017", "08.04.2017", "09.04.2017",
"10.04.2017", "11.04.2017", "12.05.2017", "13.05.2017", "14.05.2017",
"15.05.2017", "16.05.2017", "17.05.2017", "18.05.2017", "19.05.2017",
"20.05.2017", "21.05.2017", "22.05.2017", "23.05.2017", "24.05.2017",
"25.05.2017", "26.05.2017", "27.05.2017", "28.05.2017", "29.05.2017",
"30.05.2017", "31.05.2017"), class = "factor"), client = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Horns and hooves", "Kornev & Co."
), class = "factor"), stuff = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("chickens", "hooves", "Oysters"), class = "factor"),
Продажи = c(374L, 12L, 120L, 242L, 227L, 268L, 280L, 419L,
12L, 172L, 336L, 117L, 108L, 150L, 90L, 117L, 116L, 146L,
120L, 211L, 213L, 67L, 146L, 118L, 152L, 122L, 201L, 497L,
522L, 65L, 268L, 441L, 247L, 348L, 445L, 477L, 62L, 226L,
476L, 306L)), .Names = c("Data", "client", "stuff", "Продажи"
), class = "data.frame", row.names = c(NA, -40L))
of course I can manually separate three datasets
horns and hooves + hooves
Horns and hooves + chickens
Kornev & Co. + oysters
but what to do in the case when I have a huge dataset and there are hundreds of groups. Do not manually split.
Is it possible to split it in R into groups and then perform a forecast?
the code for forecast is simple
The first i do so
library(forecast)
library(lubridate)
msts <- msts(ts$sales,seasonal.periods = c(7,365.25),start = decimal_date(as.Date("2017-05-12")))
plot(msts, main="sales", xlab="Year", ylab="sales")
tbats <- tbats(msts)
plot(tbats, main="Multiple Season Decomposition")
sp<- predict(tbats,h=14) #14 days forecast
plot(sp, main = "TBATS Forecast", include=14)
print(sp)
if the result does not suit me, I'm perform forecast via dummy variables
tsw <- ts(ts$Sales, start = decimal_date(as.Date("2017-05-12")), frequency = 7)
View(tsw)
mytslm <- tslm(tsw ~ trend + season)
print(mytslm)
residarima1 <- auto.arima(mytslm$residuals)
residualsArimaForecast <- forecast(residarima1, h=14)
residualsF <- as.numeric(residualsArimaForecast$mean)
regressionForecast <- forecast(mytslm,h=14)
regressionF <- as.numeric(regressionForecast$mean)
forecastR <- regressionF+residualsF
print(forecastR)

Answer 1

You can use split to split the data into groups by a combination of factors, in this case columns client and stuff.
group_list <- split(mydat, list(mydat$client, mydat$stuff))
group_list <- group_list[sapply(group_list, function(x) nrow(x) != 0)]
Then you can use this list and lapply any function you want. The following is how you would perform your first forecast. Note that I have separated the forecast code from the plots code and that each step of the forecast is done by one function, first apply function msts and produce a list of such objects, then apply function tbats and produce another list.
fun_msts <- function(ts){
msts(ts$Sales, seasonal.periods = c(7,365.25), start = decimal_date(as.Date("2017-05-12")))
}
fun_sp <- function(m){
tbats <- tbats(m)
predict(tbats, h=14) #14 days forecast
}
msts_list <- lapply(group_list, fun_msts)
sp_list <- lapply(msts_list, fun_sp)
Now if you want to, you can plot the results. In order to do that, define two other functions to be lapplyed.
plot_msts <- function(m, new.window = TRUE){
if(new.window) windows()
plot(m, main="Sales", xlab="Year", ylab="Sales")
}
plot_sp <- function(sp, new.window = TRUE){
if(new.window) windows()
plot(sp, main = "TBATS Forecast", include = 14)
}
lapply(msts_list, plot_msts)
lapply(sp_list, plot_sp)
In these functions a new graphic device is open with function windows. If you are not using Microsoft Windows or if you want to open another type of device, change that instruction but keep the if(new.window).
EDIT.
As for the regression with dummy variables, you can do the following.
fun_tslm <- function(x, start = "2017-05-12", freq = 7){
tsw <- ts(x[["Sales"]], start = decimal_date(as.Date(start)), frequency = freq)
#View(tsw)
mytslm <- tslm(tsw ~ trend + season)
mytslm
}
fun_forecast <- function(x, h = 14){
residarima1 <- auto.arima(x[["residuals"]])
residualsArimaForecast <- forecast(residarima1, h = h)
residualsF <- as.numeric(residualsArimaForecast$mean)
regressionForecast <- forecast(x, h = h)
regressionF <- as.numeric(regressionForecast$mean)
forecastR <- regressionF + residualsF
forecastR
}
tslm_list <- lapply(group_list, fun_tslm)
fore_list <- lapply(tslm_list, fun_forecast)