r - Date format error while forecasting using tsibble objects

Question

I have converted a normal DF into a tsibble object and used that for my time-series forecasting. While fitting the model I experience the date format error- "Error in decimal_date.default(x) : date(s) not in POSIXt or Date format". As you could see from the below code- the converted tsibble object clearly identifies column "Week.1" as week date type. Could you please help me clarify why I'm still getting the date format when I fit forecast models to the tsibble object?

library(dplyr)
library(tsibble)
library(fpp3)
library(forecast)
library(fable)

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), isoweek = c("2019-W01-1", "2019-W02-1", "2019-W03-1", 
                                                                                                                                               "2019-W04-1", "2019-W05-1", "2019-W06-1", "2019-W07-1", "2019-W08-1", 
                                                                                                                                               "2019-W09-1", "2019-W10-1", "2019-W11-1", "2019-W12-1", "2019-W13-1", 
                                                                                                                                               "2019-W14-1", "2019-W15-1", "2019-W16-1", "2019-W17-1", "2019-W18-1", 
                                                                                                                                               "2019-W19-1", "2019-W20-1", "2019-W21-1", "2019-W22-1", "2019-W23-1", 
                                                                                                                                               "2019-W24-1", "2019-W25-1", "2019-W26-1", "2019-W27-1", "2019-W28-1", 
                                                                                                                                               "2019-W29-1", "2019-W30-1", "2019-W31-1", "2019-W32-1", "2019-W33-1", 
                                                                                                                                               "2019-W34-1", "2019-W35-1", "2019-W36-1", "2019-W37-1", "2019-W38-1", 
                                                                                                                                               "2019-W39-1", "2019-W40-1", "2019-W41-1", "2019-W42-1", "2019-W43-1", 
                                                                                                                                               "2019-W44-1", "2019-W45-1", "2019-W46-1", "2019-W47-1", "2019-W48-1", 
                                                                                                                                               "2019-W49-1", "2019-W50-1", "2019-W51-1", "2019-W52-1", "2020-W01-1", 
                                                                                                                                               "2020-W02-1", "2020-W03-1", "2020-W04-1", "2020-W05-1", "2020-W06-1", 
                                                                                                                                               "2020-W07-1", "2020-W08-1", "2020-W09-1", "2020-W10-1", "2020-W11-1", 
                                                                                                                                               "2020-W12-1", "2020-W13-1", "2020-W14-1", "2020-W15-1", "2020-W16-1", 
                                                                                                                                               "2020-W17-1", "2020-W18-1", "2020-W19-1", "2020-W20-1", "2020-W21-1", 
                                                                                                                                               "2020-W22-1", "2020-W23-1", "2020-W24-1", "2020-W25-1", "2020-W26-1", 
                                                                                                                                               "2020-W27-1", "2020-W28-1", "2020-W29-1", "2020-W30-1", "2020-W31-1", 
                                                                                                                                               "2020-W32-1", "2020-W33-1", "2020-W34-1", "2020-W35-1", "2020-W36-1", 
                                                                                                                                               "2020-W37-1", "2020-W38-1", "2020-W39-1", "2020-W40-1", "2020-W41-1", 
                                                                                                                                               "2020-W42-1", "2020-W43-1", "2020-W44-1", "2020-W45-1", "2020-W46-1", 
                                                                                                                                               "2020-W47-1", "2020-W48-1", "2020-W49-1", "2020-W50-1", "2020-W51-1", 
                                                                                                                                               "2020-W52-1", "2020-W53-1", "2021-W01-1", "2021-W02-1", "2021-W03-1", 
                                                                                                                                               "2021-W04-1", "2021-W05-1", "2021-W06-1", "2021-W07-1", "2021-W08-1", 
                                                                                                                                               "2021-W09-1", "2021-W10-1", "2021-W11-1", "2021-W12-1", "2021-W13-1", 
                                                                                                                                               "2021-W14-1", "2021-W15-1", "2021-W16-1", "2021-W17-1", "2021-W18-1", 
                                                                                                                                               "2021-W19-1", "2021-W20-1", "2021-W21-1", "2021-W22-1", "2021-W23-1", 
                                                                                                                                               "2021-W24-1", "2021-W25-1", "2021-W26-1", "2021-W27-1", "2021-W28-1", 
                                                                                                                                               "2021-W29-1", "2021-W30-1", "2021-W31-1", "2021-W32-1", "2021-W33-1", 
                                                                                                                                               "2021-W34-1", "2021-W35-1", "2021-W36-1", "2021-W37-1", "2021-W38-1", 
                                                                                                                                               "2021-W39-1", "2021-W40-1", "2021-W41-1", "2021-W42-1", "2021-W43-1"
                                                                                                                            ), date = structure(c(17896, 17903, 17910, 17917, 17924, 17931, 
                                                                                                                                                  17938, 17945, 17952, 17959, 17966, 17973, 17980, 17987, 17994, 
                                                                                                                                                  18001, 18008, 18015, 18022, 18029, 18036, 18043, 18050, 18057, 
                                                                                                                                                  18064, 18071, 18078, 18085, 18092, 18099, 18106, 18113, 18120, 
                                                                                                                                                  18127, 18134, 18141, 18148, 18155, 18162, 18169, 18176, 18183, 
                                                                                                                                                  18190, 18197, 18204, 18211, 18218, 18225, 18232, 18239, 18246, 
                                                                                                                                                  18253, 18260, 18267, 18274, 18281, 18288, 18295, 18302, 18309, 
                                                                                                                                                  18316, 18323, 18330, 18337, 18344, 18351, 18358, 18365, 18372, 
                                                                                                                                                  18379, 18386, 18393, 18400, 18407, 18414, 18421, 18428, 18435, 
                                                                                                                                                  18442, 18449, 18456, 18463, 18470, 18477, 18484, 18491, 18498, 
                                                                                                                                                  18505, 18512, 18519, 18526, 18533, 18540, 18547, 18554, 18561, 
                                                                                                                                                  18568, 18575, 18582, 18589, 18596, 18603, 18610, 18617, 18624, 
                                                                                                                                                  18631, 18638, 18645, 18652, 18659, 18666, 18673, 18680, 18687, 
                                                                                                                                                  18694, 18701, 18708, 18715, 18722, 18729, 18736, 18743, 18750, 
                                                                                                                                                  18757, 18764, 18771, 18778, 18785, 18792, 18799, 18806, 18813, 
                                                                                                                                                  18820, 18827, 18834, 18841, 18848, 18855, 18862, 18869, 18876, 
                                                                                                                                                  18883, 18890, 18897, 18904, 18911, 18918, 18925), class = "Date")), row.names = c(NA, 
                                                                                                                                                                                                                                    148L), class = "data.frame")

# 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)
  )

View(Original.df)

# 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,-isoweek) %>%
  as_tsibble(index = Week.1)

Net.Production.train.df

class(Net.Production.train.df$Week.1)

#Fitting forecast model(Arima errors) to Net.Production.qty 
bestfit.Net.Prod <- list(aicc=Inf)
for(K in seq(25))
{
  fit.Net.Prod <- auto.arima(Net.Production.train.df, xreg=fourier(Net.Production.train.df, K=K), seasonal=FALSE,approximation = F)
  if(fit.Net.Prod$aicc < bestfit.Net.Prod$aicc)
  {
    bestfit.Net.Prod <- fit.Net.Prod
    bestK.Net.Prod <- K
  }
}
forecast.net.prod<- forecast(bestfit.Net.Prod,xreg = fourier(Net.Production.train.df,K=bestK.Net.Prod,h=30))
forecast.net.prod

Please advise Thank you

score 1 · Accepted Answer

You are mixing 2 different ways of doing forecasts. you either use fable or you use forecast. auto.arima is from the forecast package. Though it does work with fable, it is better to keep everything to the same package eco system. Fable is the successor of forecast. Your library loading problably conflicted somewhere.

For arima forecasts check out chapter 9.7 from Forecasting: Principles and Practice 3rd edition.

I adjusted your code to work with fable. I have included 2 ways of doing this. My preference is the second one, because then you can see the difference in AICc values and see that they are very close to each other.

library(fpp3)

#... your code until just before the loop

# placeholder for the AICc
bestfit.Net.AICc <- Inf


for(K in seq(25)){
  fit <- Net.Production.train.df %>% 
    model(ARIMA(Net.Production.Qty ~ fourier(K = K), approximation = FALSE))
  
  if(purrr::pluck(glance(fit), "AICc") < bestfit.Net.AICc)
  {
    bestfit.Net.AICc <- purrr::pluck(glance(fit), "AICc")
    bestfit.Net.Prod <- fit
    bestK.Net.Prod <- K
  }
}

bestK.Net.Prod # in my case 13
glance(bestfit.Net.Prod)

# A tibble: 1 x 8
  .model                                                            sigma2 log_lik   AIC  AICc   BIC ar_roots ma_roots
  <chr>                                                              <dbl>   <dbl> <dbl> <dbl> <dbl> <list>   <list>  
1 ARIMA(Net.Production.Qty ~ fourier(K = K), approximation = FALSE) 96156.   -822. 1702. 1722. 1782. <cpl [0~ <cpl [2~


# run a forecast and plot it 
bestfit.Net.Prod %>% 
  forecast(h = 30) %>% 
  autoplot(Net.Production.train.df)

Second option:

#... your code until just before the loop

fit_all_models <- list()

for(K in seq(25)){
  fit <- Net.Production.train.df %>% 
    model(ARIMA(Net.Production.Qty ~ fourier(K = K), approximation = FALSE))
  names(fit) <- paste0("arima_", K)
  
  fit_all_models <- bind_cols(fit_all_models, fit)
}

glance(fit_all_models) %>% arrange(AICc) %>% select(.model:BIC)
# A tibble: 25 x 6
   .model    sigma2 log_lik   AIC  AICc   BIC
   <chr>      <dbl>   <dbl> <dbl> <dbl> <dbl>
 1 arima_13  96156.   -822. 1702. 1722. 1782.
 2 arima_11 104327.   -829. 1709. 1723. 1778.
 3 arima_12 102962.   -827. 1709. 1726. 1783.
 4 arima_14  95447.   -820. 1702. 1726. 1788.
 5 arima_10 108961.   -833. 1713. 1726. 1780.
 6 arima_5  127801.   -848. 1725. 1730. 1767.
 7 arima_8  117956.   -839. 1721. 1730. 1779.
 8 arima_15  95685.   -819. 1704. 1731. 1795.
 9 arima_6  127660.   -846. 1727. 1733. 1774.
10 arima_9  123129.   -842. 1724. 1733. 1779.
# ... with 15 more rows

best_model <- glance(fit_all_models) %>% 
  filter(AICc == min(AICc)) %>% 
  select(.model) %>% 
  as.character

# run a forecast and plot it 
fit_all_models %>% 
  select(best_model) %>% 
  forecast(h = 30) %>% 
  autoplot(Net.Production.train.df)

r - Date format error while forecasting using tsibble objects

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