CODEWITHSUNDEEP
rtime-seriesforecast
Michael
Michael

Reputation: 575

Forecast multiple time-series in R using

Consider an random data.frame:

d <- data.frame(replicate(10,sample(0:1,1000,rep=TRUE)))

I want to consider each row as a unique time-series (in this case for ten years). So first, I need to transform the data to time-series. I have tried the following code:

d1 <- ts(d, start=2000, end=2009)

However, this code consider the time-series as one long time-series for 100 years I think. In my case I want 1,000 unique time-series for 10 years.

And then I want to forecast each 1,000 time-series (let's say 1 year). By using the following code:

fit <- tslm(d1~trend) fcast <- forecast(fit, h=1) plot(fcast)

I get one forecast (since I in my dataset, d1, only consider one time-series).

Can anyone help me with this?

Upvotes: 4

Views: 3035

Answers (2)

Rob Hyndman
Rob Hyndman

Reputation: 31800

@akrun shows how to do it using base R and the forecast package.

Here's how to do the same thing using the new fable package which is designed to handle this sort of thing.

library(tidyverse)
library(tsibble)
library(fable)

set.seed(1)
d <- data.frame(replicate(10, sample(0:1, 1000, rep = TRUE)))
# Transpose
d <- t(d)
colnames(d) <- paste("Series",seq(NCOL(d)))
# Convert to a tsibble
df <- d %>%
  as_tibble() %>%
  mutate(time = 1:10) %>%
  gather(key = "Series", value = "value", -time) %>%
  as_tsibble(index = time, key = Series)
df
#> # A tsibble: 10,000 x 3 [1]
#> # Key:       Series [1,000]
#>     time Series   value
#>    <int> <chr>    <int>
#>  1     1 Series 1     0
#>  2     2 Series 1     1
#>  3     3 Series 1     0
#>  4     4 Series 1     0
#>  5     5 Series 1     1
#>  6     6 Series 1     0
#>  7     7 Series 1     0
#>  8     8 Series 1     0
#>  9     9 Series 1     1
#> 10    10 Series 1     0
#> # … with 9,990 more rows
# Fit models
fit <- model(df, TSLM(value ~ trend()))
# Compute forecasts
fcast <- forecast(fit, h = 1)
# Plot forecasts for one series
fcast %>%
  filter(Series == "Series 1") %>%
  autoplot(df)

Created on 2019-10-11 by the reprex package (v0.3.0)

Upvotes: 2

akrun
akrun

Reputation: 886938

If we are looking for creating time series for each column, then loop through the columns of the dataset with lapply and create it

library(forecast)
lst1 <- lapply(d, ts, start = 2000, end = 2009)
#If we want to split by `row`
#lst1 <- lapply(asplit(as.matrix(d), 1), ts, start = 2000, end = 2009)
par(mfrow = c(5, 2))
lapply(lst1, function(x) {
        fit <- tslm(x ~ trend)
        fcast <- forecast(fit, h = 1)
        plot(fcast)
   })

enter image description here

Upvotes: 4

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