寓言package中的不规则时间序列

Question

In the tsibble package and fable package, I think I read somewhere that we can handle irregular time series. I could not find anything with examples on how to do it. Some questions I have are:

1. Do I have to convert irregular timeseries to a regular one before I can model? (So far what I know is that we need to convert irregular time series to a regular one. Please let me know if its is not the case? and if not then what are some models that do not need regular time series?)
2. What are the tools and models in tidyverts/tsibble/ fable /fabletools to handle irregular timeseries?

Are there any questions/ links where I can see a working example? eg This question uses zoo/xts to handle it .

I saw some capabilities related to that in zoo/xts, which is always good but I am spinning my wheels on fable and trying to get it to work.

for a sample dataset we can use

    DF <- structure(list(station = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L), Time = structure(c(1L, 2L, 3L, 5L, 7L, 1L, 2L, 4L, 6L, 8L
), .Label = c("01-01-1974", "01-02-1974", "01-03-1974", "01-04-1974", 
"01-05-1974", "01-06-1974", "01-07-1974", "01-08-1974"), class = "factor"), 
    WaterTemp = c(5, 5, 8.6000004, 8.1333332, 12.7999999, 5, 
    5, 8.6000004, 8.1333332, 12.7999999)), .Names = c("station", 
"Time", "WaterTemp"), class = "data.frame", row.names = c(NA, 
-10L))

Answer 1

Most models available in {fable} require the observations to be regular, and a lot of models also require that there are no gaps in the data. An example model which supports irregular data is fable::TSLM() .

The above example data is typically considered 'regular' but with gaps. This is because the data has a common interval of 1 month , however some months are missing in the data. Here is how a tsibble for this data can be produced:

DF <- structure(list(station = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
                                 2L), Time = structure(c(1L, 2L, 3L, 5L, 7L, 1L, 2L, 4L, 6L, 8L
                                 ), .Label = c("01-01-1974", "01-02-1974", "01-03-1974", "01-04-1974", 
                                               "01-05-1974", "01-06-1974", "01-07-1974", "01-08-1974"), class = "factor"), 
                     WaterTemp = c(5, 5, 8.6000004, 8.1333332, 12.7999999, 5, 
                                   5, 8.6000004, 8.1333332, 12.7999999)), .Names = c("station", 
                                                                                     "Time", "WaterTemp"), class = "data.frame", row.names = c(NA, 
                                                                                                                                               -10L))

# Fix $Time to a valid yearmonth index variable
library(tsibble)
library(dplyr)
DF <- DF %>% 
  mutate(Time = yearmonth(as.Date(format(Time), format = "%d-%m-%Y")))
DF
#>    station     Time WaterTemp
#> 1        1 1974 Jan  5.000000
#> 2        1 1974 Feb  5.000000
#> 3        1 1974 Mar  8.600000
#> 4        1 1974 May  8.133333
#> 5        1 1974 Jul 12.800000
#> 6        2 1974 Jan  5.000000
#> 7        2 1974 Feb  5.000000
#> 8        2 1974 Apr  8.600000
#> 9        2 1974 Jun  8.133333
#> 10       2 1974 Aug 12.800000

# Create a 'regular' tsibble (with gaps)
as_tsibble(DF, key = "station", index = "Time")
#> # A tsibble: 10 x 3 [1M]
#> # Key:       station [2]
#>    station     Time WaterTemp
#>      <int>    <mth>     <dbl>
#>  1       1 1974 Jan      5   
#>  2       1 1974 Feb      5   
#>  3       1 1974 Mar      8.60
#>  4       1 1974 May      8.13
#>  5       1 1974 Jul     12.8 
#>  6       2 1974 Jan      5   
#>  7       2 1974 Feb      5   
#>  8       2 1974 Apr      8.60
#>  9       2 1974 Jun      8.13
#> 10       2 1974 Aug     12.8

To fill in the gaps of this dataset - similarly to what is shown in the linked question - you can use the tsibble::fill_gaps() function. This makes the data compatible with models that support missing values, but don't support gaps in the data such as fable::ARIMA() .

# Create a 'regular' tsibble (with gaps) then complete the gaps
as_tsibble(DF, key = "station", index = "Time") %>% 
  fill_gaps()
#> # A tsibble: 15 x 3 [1M]
#> # Key:       station [2]
#>    station     Time WaterTemp
#>      <int>    <mth>     <dbl>
#>  1       1 1974 Jan      5   
#>  2       1 1974 Feb      5   
#>  3       1 1974 Mar      8.60
#>  4       1 1974 Apr     NA   
#>  5       1 1974 May      8.13
#>  6       1 1974 Jun     NA   
#>  7       1 1974 Jul     12.8 
#>  8       2 1974 Jan      5   
#>  9       2 1974 Feb      5   
#> 10       2 1974 Mar     NA   
#> 11       2 1974 Apr      8.60
#> 12       2 1974 May     NA   
#> 13       2 1974 Jun      8.13
#> 14       2 1974 Jul     NA   
#> 15       2 1974 Aug     12.8

An irregular time series can be created using regular = FALSE . This is typically useful if you're working with event data. In this case you would rarely want to fill the gaps, because there are so many.

# Create an 'irregular' tsibble (no concept of gaps)
as_tsibble(DF, key = "station", index = "Time", regular = FALSE)
#> # A tsibble: 10 x 3 [!]
#> # Key:       station [2]
#>    station     Time WaterTemp
#>      <int>    <mth>     <dbl>
#>  1       1 1974 Jan      5   
#>  2       1 1974 Feb      5   
#>  3       1 1974 Mar      8.60
#>  4       1 1974 May      8.13
#>  5       1 1974 Jul     12.8 
#>  6       2 1974 Jan      5   
#>  7       2 1974 Feb      5   
#>  8       2 1974 Apr      8.60
#>  9       2 1974 Jun      8.13
#> 10       2 1974 Aug     12.8

^{Created on 2021-02-09 by the reprex package (v0.3.0)}