Agfdhyk

Time series data consists of:

Product (categorical); ProductGroup (categorical); Country (categorical); YearSinceProductLaunch (numeric); SalesAtLaunchYear (numeric)

Only "SalesAtLaunchYear" data has some missing values which needs to be imputed.

For some products, there is complete data, i.e. sales data exists for the launch year 1,2 and up to now.

Some other products, however, contain missing sales data only for the early years since launch. Products have different age, therefore sometimes there are 2 years since launch that are missing, sometimes there are 10 years, which depends on the product.

I am interested to find a model in R that can impute the missing time series data gaps. I have tried MICE by setting the model for "SalesAtLaunchYear" as random forest, but I am still getting some very high values of sales especially at the beginning of the product's launch. I am ensuring that at Year 0, all sales are 0 to avoid negative values. The data frame has 20000 rows with 300 unique products.

testdf = tibble::tribble(

  ~Country,   ~ProductGroup,   ~Product, ~YearSinceProductLaunch, ~SalesAtLaunchYear,

      "CA", "ProductGroup1", "Product1",                      0L,                  0,

      "CA", "ProductGroup1", "Product1",                      1L,                 NA,

      "CA", "ProductGroup1", "Product1",                      2L,                 NA,

      "CA", "ProductGroup1", "Product1",                      3L,                 NA,

      "CA", "ProductGroup1", "Product1",                      4L,                 NA,

      "CA", "ProductGroup1", "Product1",                      5L,        206034.9814,

      "CA", "ProductGroup1", "Product1",                      6L,        170143.2623,

      "CA", "ProductGroup1", "Product1",                      7L,        212541.9306,

      "CA", "ProductGroup1", "Product1",                      8L,         270663.199,

      "CA", "ProductGroup1", "Product1",                      9L,        736738.3755,

      "CA", "ProductGroup1", "Product1",                     10L,        2579723.981,

      "CA", "ProductGroup1", "Product1",                     11L,        4964319.496,

      "CA", "ProductGroup1", "Product1",                     12L,         6864985.16,

      "CA", "ProductGroup1", "Product1",                     13L,        8793292.386,

      "CA", "ProductGroup1", "Product1",                     14L,        11416033.38,

      "IT", "ProductGroup2", "Product2",                      0L,                  0,

      "IT", "ProductGroup2", "Product2",                      1L,                 NA,

      "IT", "ProductGroup2", "Product2",                      2L,                 NA,

      "IT", "ProductGroup2", "Product2",                      3L,                 NA,

      "IT", "ProductGroup2", "Product2",                      4L,                 NA,

      "IT", "ProductGroup2", "Product2",                      5L,                 NA,

      "IT", "ProductGroup2", "Product2",                      6L,                 NA,

      "IT", "ProductGroup2", "Product2",                      7L,                 NA,

      "IT", "ProductGroup2", "Product2",                      8L,                 NA,

      "IT", "ProductGroup2", "Product2",                      9L,                 NA,

      "IT", "ProductGroup2", "Product2",                     10L,                 NA,

      "IT", "ProductGroup2", "Product2",                     11L,                 NA,

      "IT", "ProductGroup2", "Product2",                     12L,                 NA,

      "IT", "ProductGroup2", "Product2",                     13L,        30806222.96,

      "IT", "ProductGroup2", "Product2",                     14L,           31456272,

      "IT", "ProductGroup2", "Product2",                     15L,        31853476.78,

      "IT", "ProductGroup2", "Product2",                     16L,           30379818,

      "IT", "ProductGroup2", "Product2",                     17L,        29765448.87,

      "IT", "ProductGroup2", "Product2",                     18L,           31376234,

      "IT", "ProductGroup2", "Product2",                     19L,        32628514.81,

      "IT", "ProductGroup2", "Product2",                     20L,           32732196,

      "IT", "ProductGroup2", "Product2",                     21L,        33503784.25,

      "IT", "ProductGroup2", "Product2",                     22L,           35163372,

      "DE", "ProductGroup3", "Product3",                      0L,                  0,

      "DE", "ProductGroup3", "Product3",                      1L,         161884.081,

      "DE", "ProductGroup3", "Product3",                      2L,        7876925.474,

      "DE", "ProductGroup3", "Product3",                      3L,        12948209.55,

      "DE", "ProductGroup3", "Product3",                      4L,        13304401.76

  )





testdf$Country = as.factor(testdf$Country)

testdf$ProductGroup   = as.factor(testdf$ProductGroup)

testdf$Product  = as.factor(testdf$Product)

Probably using mice will not give you the desired results. Since it mostly uses inter-variable correlations. You are looking more for correlations in time.

My recommendation for this specific example would be to split the dataset into Country, ProductGroup, Product groups and perform imputation on these with a time series imputation package.

Looking at your data I think something like the function na.interpolation from package imputeTS would already do a good job.

That is how you call it:

library("imputeTS")

na.interpolation(yourTimeSeries)

You would have to call it multiple times for each time series you created out of the each Country, ProductGroup, Product.

You can also just run

 na.interpolation(testdf$SalesAtLaunchYear)

On your whole dataset which is easier - in the example you showed this would also work. (might lead to problems if the rest is structured differently or you are using a different algorithm from imputeTS package)