Handling missing multiple time series data in a csv file

#1

Dear all, I have a large data set which contains NA in multiple column, how can I use TS to impute these NA value.

First, I am finding a way to change the data table into TS.

Many thanks.

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#2

We don't really have enough info to help you out. Could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

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#3

Thx for your advice as I don't know how to upload my csv file. I run a code to show the list of my datatable and uploaded for reference.

Many thanks.

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#4

Please read the link I gave you, there is no need to upload a csv file and posting screenshots of your data is not a good thing to do here.

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#5

More details, column PM_Dongsi, PM_Dongsihuan and PM_Nongzhanguan are randomly missing value, and these missing value are distributed as follows:

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#7

Sorry guys my mistake as I am new to R also forum, I should notice it first. Below is my coding after using reprex:

trialdata <-tibble::tribble(
~No, ~year, ~month, ~day, ~hour, ~season, ~PM_Dongsi, ~PM_Dongsihuan, ~PM_Nongzhanguan, ~PM_US_Post, ~HUMI, ~PRES, ~TEMP,
  1,  2013,      1,    1,     0,       4,         NA,             NA,               NA,          31,    67,  1018,    -5,
  2,  2013,      1,    1,     1,       4,         NA,             NA,               NA,          32,    73,  1017,    -7,
  3,  2013,      1,    1,     2,       4,         NA,             NA,               NA,          21,    73,  1017,    -7,
  4,  2013,      1,    1,     3,       4,         NA,             NA,               NA,          16,    72,  1018,   -10,
  5,  2013,      1,    1,     4,       4,         NA,             NA,               NA,          15,    66,  1018,   -10,
  6,  2013,      1,    1,     5,       4,         NA,             NA,               NA,           9,    78,  1019,   -12,
  7,  2013,      1,    1,     6,       4,         NA,             NA,               NA,           9,    72,  1020,   -11,
  8,  2013,      1,    1,     7,       4,         NA,             NA,               NA,           7,    45,  1020,    -6,
  9,  2013,      1,    1,     8,       4,         NA,             NA,               NA,          14,    38,  1021,    -6,
 10,  2013,      1,    1,     9,       4,         NA,             NA,               NA,          11,    32,  1022,    -5,
 11,  2013,      1,    1,    10,       4,         NA,             NA,               NA,           9,    21,  1023,    -3,
 12,  2013,      1,    1,    11,       4,         NA,             NA,               NA,          10,    21,  1024,    -3,
 13,  2013,      1,    1,    12,       4,         NA,             NA,               NA,          16,    19,  1023,    -3,
 14,  2013,      1,    1,    13,       4,         NA,             NA,               NA,          17,    21,  1023,    -4,
 15,  2013,      1,    1,    14,       4,         NA,             NA,               NA,          15,    21,  1024,    -4,
 16,  2013,      1,    1,    15,       4,         NA,             NA,               NA,          13,    19,  1025,    -5,
 17,  2013,      1,    1,    16,       4,         NA,             NA,               NA,          16,    20,  1026,    -6,
 18,  2013,      1,    1,    17,       4,         NA,             NA,               NA,          15,    22,  1027,    -7,
 19,  2013,      1,    1,    18,       4,         NA,             NA,               NA,          20,    20,  1028,    -8,
 20,  2013,      1,    1,    19,       4,         NA,             NA,               NA,          17,    23,  1029,    -9,
 21,  2013,      1,    1,    20,       4,         NA,             NA,               NA,          16,    25,  1030,   -10,
 22,  2013,      1,    1,    21,       4,         NA,             NA,               NA,          19,    23,  1031,   -10,
 23,  2013,      1,    1,    22,       4,         NA,             NA,               NA,          13,    25,  1032,   -11,
 24,  2013,      1,    1,    23,       4,         NA,             NA,               NA,          19,    23,  1033,   -11,
 25,  2013,      1,    2,     0,       4,         NA,             NA,               NA,          15,    25,  1033,   -12,
 26,  2013,      1,    2,     1,       4,         NA,             NA,               NA,          19,    25,  1033,   -12,
 27,  2013,      1,    2,     2,       4,         NA,             NA,               NA,          16,    25,  1034,   -12,
 28,  2013,      1,    2,     3,       4,         NA,             NA,               NA,          17,    25,  1035,   -12,
 29,  2013,      1,    2,     4,       4,         NA,             NA,               NA,          17,    25,  1035,   -12,
 30,  2013,      1,    2,     5,       4,         NA,             NA,               NA,          17,    27,  1036,   -13,
 31,  2013,      1,    2,     6,       4,         NA,             NA,               NA,          15,    27,  1036,   -13,
 32,  2013,      1,    2,     7,       4,         NA,             NA,               NA,          13,    27,  1036,   -13,
 33,  2013,      1,    2,     8,       4,         NA,             NA,               NA,          15,    27,  1037,   -12,
 34,  2013,      1,    2,     9,       4,         NA,             NA,               NA,          15,    25,  1039,   -11
)

Many thanks

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#8

You were almost there.

When you paste the code in a markdown environment, placing it in between a pair of ``` makes it more readable. I edited your post, and you may check what modifications did I make.

I'm not that familiar with time series, but I don't think that if all observations of a column is missing, you'll be able to impute the missing values.

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closed #9

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