Class of a tsibble object vs class of a ts object

arvind · December 6, 2021, 8:11am

Hello All,

Just wanted to clarify the difference in converting a normal df into a ts object vs tsibble object.
In ts object we are specifying the frequency and we wont be including the date column while we convert to a ts object.
However in the tsibble object, we can directly include a date column by saying the date column as a index so the frequency will be considered automatically.

Class of a ts object is shown as "ts matrix" whereas the class of a tsibble object shows as a "dataframe". Does having a tsibble object for forecasting would still give the same results as a ts object? Also since tsibble being a dataframe how does it impact the performance when passing Arima or Arimax to a tsibble to that of a normal ts object?
The date column which I give as an index while converting to a tsibble object is not visible when I pass the View function to the tsibble converted object. Is it possible to View the index column of a tsibble object?

Please see the below example where I'm trying to convert a df to a tsibble object, while doing that I'm trying to remove some of the columns and intend to keep a univariate time series but i get this error- "Error in manip_at(.tbl, .vars, .funs, enquo(.funs), caller_env(), .include_group_vars = TRUE, :
argument ".funs" is missing, with no default"

Please advise

library(tidyverse)
library(astsa)
library(vars)
library(fpp3)
#original data frame
Original.df<- structure(list(YearWeek = c("201901", "201902", "201903", "201904", 
                                          "201905", "201906", "201907", "201908", "201909", "201910", "201911", 
                                          "201912", "201913", "201914", "201915", "201916", "201917", "201918", 
                                          "201919", "201920", "201921", "201922", "201923", "201924", "201925", 
                                          "201926", "201927", "201928", "201929", "201930", "201931", "201932", 
                                          "201933", "201934", "201935", "201936", "201937", "201938", "201939", 
                                          "201940", "201941", "201942", "201943", "201944", "201945", "201946", 
                                          "201947", "201948", "201949", "201950", "201951", "201952", "202001", 
                                          "202002", "202003", "202004", "202005", "202006", "202007", "202008", 
                                          "202009", "202010", "202011", "202012", "202013", "202014", "202015", 
                                          "202016", "202017", "202018", "202019", "202020", "202021", "202022", 
                                          "202023", "202024", "202025", "202026", "202027", "202028", "202029", 
                                          "202030", "202031", "202032", "202033", "202034", "202035", "202036", 
                                          "202037", "202038", "202039", "202040", "202041", "202042", "202043", 
                                          "202044", "202045", "202046", "202047", "202048", "202049", "202050", 
                                          "202051", "202052", "202053", "202101", "202102", "202103", "202104", 
                                          "202105", "202106", "202107", "202108", "202109", "202110", "202111", 
                                          "202112", "202113", "202114", "202115", "202116", "202117", "202118", 
                                          "202119", "202120", "202121", "202122", "202123", "202124", "202125", 
                                          "202126", "202127", "202128", "202129", "202130", "202131", "202132", 
                                          "202133", "202134", "202135", "202136", "202137", "202138", "202139", 
                                          "202140", "202141", "202142", "202143"), Shipment = c(418, 1442, 
                                                                                                1115, 1203, 1192, 1353, 1191, 1411, 933, 1384, 1362, 1353, 1739, 
                                                                                                1751, 1595, 1380, 1711, 2058, 1843, 1602, 2195, 2159, 2009, 1812, 
                                                                                                2195, 1763, 821, 1892, 1781, 2071, 1789, 1789, 1732, 1384, 1435, 
                                                                                                1247, 1839, 2034, 1963, 1599, 1596, 1548, 1084, 1350, 1856, 1882, 
                                                                                                1979, 1021, 1311, 2031, 1547, 591, 724, 1535, 1268, 1021, 1269, 
                                                                                                1763, 1275, 1411, 1847, 1379, 1606, 1473, 1180, 926, 800, 840, 
                                                                                                1375, 1755, 1902, 1921, 1743, 1275, 1425, 1088, 1416, 1168, 842, 
                                                                                                1185, 1570, 1435, 1209, 1470, 1368, 1926, 1233, 1189, 1245, 1465, 
                                                                                                1226, 887, 1489, 1369, 1358, 1179, 1200, 1226, 1066, 823, 1913, 
                                                                                                2308, 1842, 910, 794, 1098, 1557, 1417, 1851, 1876, 1010, 160, 
                                                                                                1803, 1607, 1185, 1347, 1700, 981, 1191, 1058, 1464, 1513, 1333, 
                                                                                                1169, 1294, 978, 962, 1254, 987, 1290, 758, 436, 579, 636, 614, 
                                                                                                906, 982, 649, 564, 502, 274, 473, 506, 902, 639, 810, 398, 488
                                          ), Production = c(0, 198, 1436, 1055, 1396, 1330, 1460, 1628, 
                                                            1513, 1673, 1737, 1274, 1726, 1591, 2094, 1411, 2009, 1909, 1759, 
                                                            1693, 1748, 1455, 2078, 1717, 1737, 1886, 862, 1382, 1779, 1423, 
                                                            1460, 1454, 1347, 1409, 1203, 1235, 1397, 1563, 1411, 1455, 1706, 
                                                            688, 1446, 1336, 1618, 1404, 1759, 746, 1560, 1665, 1317, 0, 
                                                            441, 1390, 1392, 1180, 1477, 1265, 1485, 1495, 1543, 1584, 1575, 
                                                            1609, 1233, 1420, 908, 1008, 1586, 1392, 1385, 1259, 1010, 973, 
                                                            1053, 905, 1101, 1196, 891, 1033, 925, 889, 1136, 1058, 1179, 
                                                            1047, 967, 900, 904, 986, 1014, 945, 1030, 1066, 1191, 1143, 
                                                            1292, 574, 1174, 515, 1296, 1315, 1241, 0, 0, 1182, 1052, 1107, 
                                                            1207, 1254, 1055, 258, 1471, 1344, 1353, 1265, 1444, 791, 1397, 
                                                            1186, 1264, 1032, 949, 1059, 954, 798, 956, 1074, 1136, 1209, 
                                                            975, 833, 994, 1127, 1153, 1202, 1234, 1336, 1484, 1515, 1151, 
                                                            1175, 976, 1135, 1272, 869, 1900, 1173), Net.Production.Qty = c(22, 
                                                                                                                            188, 1428, 1031, 1382, 1368, 1456, 1578, 1463, 1583, 1699, 1318, 
                                                                                                                            1582, 1537, 2118, 1567, 1961, 1897, 1767, 1603, 1666, 1419, 2186, 
                                                                                                                            1621, 1677, 1840, 698, 1290, 1411, 927, 1754, 1222, 1411, 1549, 
                                                                                                                            1491, 1359, 1179, 1945, 1463, 1465, 1764, 764, 810, 1308, 1830, 
                                                                                                                            1542, 1695, 544, 1482, 1673, 1659, 0, 445, 1358, 1364, 1224, 
                                                                                                                            1417, 1239, 1387, 1595, 1469, 1624, 1643, 1763, 1217, 1456, 568, 
                                                                                                                            1290, 1666, 1428, 1327, 773, 1118, 1231, 1143, 921, 1083, 1124, 
                                                                                                                            935, 903, 937, 849, 1132, 1032, 1143, 1081, 891, 886, 880, 1002, 
                                                                                                                            1072, 969, 1000, 996, 1243, 1183, 1306, 650, 1226, 553, 1306, 
                                                                                                                            1379, 1359, 0, 0, 1182, 988, 1099, 1173, 1244, 1039, 254, 1425, 
                                                                                                                            1318, 1385, 1221, 1364, 739, 1397, 1112, 1160, 924, 971, 1015, 
                                                                                                                            978, 828, 868, 994, 1090, 1165, 783, 887, 934, 1023, 1045, 1114, 
                                                                                                                            1052, 1186, 1456, 1401, 1249, 779, 430, 1625, 1498, 883, 1860, 
                                                                                                                            1101)), row.names = c(NA, 148L), class = "data.frame")

#Converting the df to accomodate leap year for weekly observations
Original.df <- Original.df %>%
  mutate(isoweek = str_replace(YearWeek,
                               "^(\\d{4})(\\d{2})$",
                               "\\1-W\\2-1"),
         date = ISOweek::ISOweek2date(isoweek))
View(Original.df)
#creating test and train data
Original.train.df<-with(Original.df,Original.df[(Original.df$date >= "2018-12-31" &
                                                   Original.df$date <= "2021-03-29"),])
Original.test.df<-with(Original.df,Original.df[(Original.df$date >= "2021-04-05" &
                                                  Original.df$date <= "2021-10-25"),])

#splitting the original train data with multiple variables to have only one variable(univariate time series)
Net.Production.train.df<-Original.train.df %>% 
                         mutate(Week.1 = ISOweek(date)) %>%
                         mutate(Week.1 = yearweek(Week.1)) %>%
                         mutate_at(vars(-YearWeek,-Shipment,-Production,-date)) %>%
                        as_tsibble(index = Week.1)

Please advise

Thank you,
Vignesh

robjhyndman · December 6, 2021, 9:54am

First, to answer your questions.

The fable package should give the same forecasts for a tsibble object as the forecast package gives for a ts object. The algorithms are the same in almost all cases, it's just the objects that are input and output that have a different class. There are a couple of minor variations in the forecast algorithms, but in most cases you shouldn't notice any differences. The main advantage in using the fable package with tsibble objects is that it allows you to fit multiple models simultaneously, whereas the forecast package only fits one model at a time.
Yes, the index column is just another variable in the tsibble and will be viewed along with all the other columns.

In your code, it is not clear what you were trying to do with the mutate_at() function. Perhaps you meant to use the select() function which allows you to remove some of the columns. The following code uses the select() function to return a tsibble object.

A tsibble object can include multiple measurement variables. When you fit models using the model() function, you choose which variable(s) you want to model.

library(dplyr)
library(tsibble)

# Converting the df to accomodate leap year for weekly observations
Original.df <- Original.df %>%
  mutate(
    isoweek =stringr::str_replace(YearWeek, "^(\\d{4})(\\d{2})$", "\\1-W\\2-1"),
    date = ISOweek::ISOweek2date(isoweek)
  )

# creating test and train data
Original.train.df <- Original.df %>%
  filter(date >= "2018-12-31", date <= "2021-03-29")
Original.test.df <- Original.df %>%
  filter(date >= "2021-04-05", date <= "2021-10-25")

# splitting the original train data with multiple variables to have only one variable(univariate time series)
Net.Production.train.df <- Original.train.df %>%
  mutate(Week.1 = yearweek(ISOweek::ISOweek(date))) %>%
  select(-YearWeek, -Shipment, -Production, -date) %>%
  as_tsibble(index = Week.1)

Net.Production.train.df
#> # A tsibble: 118 x 3 [1W]
#>    Net.Production.Qty isoweek      Week.1
#>                 <dbl> <chr>        <week>
#>  1                 22 2019-W01-1 2019 W01
#>  2                188 2019-W02-1 2019 W02
#>  3               1428 2019-W03-1 2019 W03
#>  4               1031 2019-W04-1 2019 W04
#>  5               1382 2019-W05-1 2019 W05
#>  6               1368 2019-W06-1 2019 W06
#>  7               1456 2019-W07-1 2019 W07
#>  8               1578 2019-W08-1 2019 W08
#>  9               1463 2019-W09-1 2019 W09
#> 10               1583 2019-W10-1 2019 W10
#> # … with 108 more rows

^{Created on 2021-12-06 by the reprex package (v2.0.1)}

arvind · December 6, 2021, 10:02am

@robjhyndman Thank you Sir for the clear explanation. It really helped me a lot. Is there any examples for the model() function you mentioned while choosing different variables?

robjhyndman · December 6, 2021, 10:22am

Read the associated textbook that goes with the fable package: OTexts.com/fpp3

arvind · December 6, 2021, 10:27am

Thank you Sir. Also one query though with the respect to the above example. After converting to a tsibble object, it clearly identifies Week.1 as date type. But when I fit any forecast model to this tsibble object it gives me the error- "Error in decimal_date.default(x) : date(s) not in POSIXt or Date format". As I'm not sure why there is a format error even after the tsibble object clearly identifies the Week.1 column as week

system · December 13, 2021, 10:28am

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