exponential time series for multiple time series data

Question

My data has different start and end points.

structure(list(item = c("Card", "Card", "Card", "Card", "Card", 
"Card", "Card", "Card", "battery", "battery", "battery", "battery", 
"battery", "laptop", "laptop", "laptop", "laptop", "laptop", 
"laptop", "laptop"), sales = c(20.4, 29, 26, 40, 35, 36, 28, 
41, 70, 75, 78, 99, 40, 100, 132, 123, 145, 125, 145, 124), Date = structure(c(17784, 
17791, 17798, 17805, 17812, 17819, 17826, 17833, 17608, 17615, 
17622, 17629, 17636, 17713, 17726, 17739, 17752, 17765, 17778, 
17791), class = "Date")), row.names = c(NA, -20L), class = "data.frame")

I tried doing

ts_test <- ts(multiple_ts, frequency=52)

to convert to time series but it failed

structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 20.4, 29, 26, 40, 35, 36, 28, 41, 
70, 75, 78, 99, 40, 100, 132, 123, 145, 125, 145, 124, 17784, 
17791, 17798, 17805, 17812, 17819, 17826, 17833, 17608, 17615, 
17622, 17629, 17636, 17713, 17726, 17739, 17752, 17765, 17778, 
17791), .Dim = c(20L, 3L), .Dimnames = list(NULL, c("item", "sales", 
"Date")), .Tsp = c(1, 1.36538461538462, 52), class = c("mts", 
"ts", "matrix"))

Can some one help me how to convert to time series group by item and apply exponential smoothing to each item. Thanks in Advance!

G. Grothendieck · Accepted Answer · 2018-11-12T17:21:29.337

0

Convert the data frame into a 3 column zoo object z and from z create a list of ts objects L. Apply exponential smoothing to each component of L giving HW. Then plot each of those. Note that ts objects cannot represent Date class directly so we omit the X axis and draw it ourself in pltHW.

library(zoo)

z <- read.zoo(multiple_ts, index = "Date", split = "item")
L <- lapply(as.list(z), function(x) as.ts(na.omit(x)))
HW <- lapply(L, HoltWinters, beta = FALSE, gamma = FALSE)

# given HoltWinters object x get fitted values as zooreg object
fitHW <- function(x) {
  fitted <- fitted(x)
  zooreg(fitted[, 1], as.Date(start(fitted)), frequency = frequency(fit))
}

# plot
pltHW <- function(x, sub) {
  plot(x, sub = sub, xaxt = "n")
  fit <- fit(x)
  Axis(time(fit), side = 1)
  invisible(x)
}

par.old <- par(mfrow = c(3, 1))
junk <- Map(pltHW, HW, names(HW))
par(par.old)

edited Nov 12 '18 at 17:21

answered Nov 12 '18 at 16:17

G. Grothendieck

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I tried this way,but for `junk <- Map(pltHW, HW, names(HW))` i got the following error ` Error in plot.new() : figure margins too large `.And is there any way to unlist and get in a ts model where we can see the fitted values and actual values in same frame@G. Grothendieck – riyan Nov 12 '18 at 17:07
Using the data in the question I can't reproduce that error. It gives the output shown for me. Have factored out `fitHW` in the answer and so `do.call("merge", lapply(HW, fitHW))` gives the fitted values. – G. Grothendieck Nov 12 '18 at 17:22
When I actually applied to my whole data set and `read.zoo(multiple_ts, index = "Date", split = "item")` ran this code.The error I get is `Error in merge.zoo` `series cannot be merged with non-unique index entries in a series` – riyan Nov 12 '18 at 20:21
You will need to provide reproducible code and data. – G. Grothendieck Nov 13 '18 at 00:23
How do we unnest the data which doesn't have same lengths@G.Grothendieck – riyan Nov 15 '18 at 22:27
unnesting does not require equal size components. In fact `lengths(L)` is `c(5, 8, 7)`. – G. Grothendieck Nov 15 '18 at 22:35

exponential time series for multiple time series data

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