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My goal is to make a prediction using Time Series and for that I need to create a model using the auto.arima
, but after several attempts the following error message is displayed:
Error in auto.arima(treino, seasonal = F) : No suitable ARIMA model found
In addition: Warning message:
The chosen unit root test encountered an error when testing for the first difference.
From -(): non-numeric argument to binary operator
0 differences will be used. Consider using a different unit root test.
To help understand what is happening I will leave here the data Deput.
dput(head(dados, 50))
structure(c("2016-01-01", "2016-02-01", "2016-03-01", "2016-04-01",
"2016-05-01", "2016-06-01", "2016-07-01", "2016-08-01", "2016-09-01",
"2016-10-01", "2016-11-01", "2016-12-01", "2017-01-01", "2017-02-01",
"2017-03-01", "2017-04-01", "2017-05-01", "2017-06-01", "2017-07-01",
"2017-08-01", "2017-09-01", "2017-10-01", "2017-11-01", "2017-12-01",
"2018-01-01", "2018-02-01", "2018-03-01", "2018-04-01", "2018-05-01",
"2018-06-01", "2018-07-01", "2018-08-01", "2018-09-01", "2018-10-01",
"2018-11-01", "2018-12-01", "2019-01-01", "2019-02-01", "2019-03-01",
"2019-04-01", "2019-05-01", "2019-06-01", "2019-07-01", "2016-01",
"2016-02", "2016-03", "2016-04", "2016-05", "2016-06", "2016-07",
"2016-08", "2016-09", "2016-10", "2016-11", "2016-12", "2017-01",
"2017-02", "2017-03", "2017-04", "2017-05", "2017-06", "2017-07",
"2017-08", "2017-09", "2017-10", "2017-11", "2017-12", "2018-01",
"2018-02", "2018-03", "2018-04", "2018-05", "2018-06", "2018-07",
"2018-08", "2018-09", "2018-10", "2018-11", "2018-12", "2019-01",
"2019-02", "2019-03", "2019-04", "2019-05", "2019-06", "2019-07",
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016", "2016", "2017", "2017", "2017", "2017",
"2017", "2017", "2017", "2017", "2017", "2017", "2017", "2017",
"2018", "2018", "2018", "2018", "2018", "2018", "2018", "2018",
"2018", "2018", "2018", "2018", "2019", "2019", "2019", "2019",
"2019", "2019", "2019", " 1", " 2", " 3", " 4", " 5", " 6", " 7",
" 8", " 9", "10", "11", "12", " 1", " 2", " 3", " 4", " 5", " 6",
" 7", " 8", " 9", "10", "11", "12", " 1", " 2", " 3", " 4", " 5",
" 6", " 7", " 8", " 9", "10", "11", "12", " 1", " 2", " 3", " 4",
" 5", " 6", " 7", "65", "63", "60", "59", "59", "58", "56", "56",
"57", "58", "58", "59", "59", "57", "57", "58", "60", "59", "58",
"61", "61", "64", "62", "62", "63", "63", "63", "62", "61", "62",
"62", "60", "61", "62", "62", "62", "67", "65", "66", "69", "69",
"69", "69", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5",
"5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5",
"5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5",
"5", "5", "5", "5", "5", "5", "5", "70", "68", "65", "65", "64",
"63", "61", "61", "63", "63", "63", "64", "64", "61", "62", "64",
"67", "64", "63", "65", "64", "67", "67", "69", "71", "69", "69",
"70", "67", "70", "68", "65", "68", "68", "64", "66", "73", "70",
"71", "75", "75", "74", "73", "8801", "8284", "8663", "8426",
"8757", "7807", "7909", "8085", "7342", "7766", "6917", "6524",
"6712", "5494", "6667", "5841", "6800", "5665", "6181", "6003",
"5448", "6149", "5955", "6100", "6107", "5267", "6024", "5742",
"5843", "5355", "5528", "5063", "4957", "5101", "4605", "4576",
"4887", "4496", "5400", "5867", "6147", "5620", "5301", "10162.76",
"10271.75", "10989.96", "10531.39", "11106.39", " 9847.93", "10067.73",
"10243.22", " 9073.81", " 9515.86", " 8374.93", " 8038.93", " 7971.49",
" 6494.01", " 8086.93", " 7011.61", " 8877.80", " 7118.37", " 7919.78",
" 7596.31", " 6827.23", " 7590.32", " 7301.27", " 7401.93", " 7639.75",
" 6677.50", " 7947.16", " 7399.59", " 7678.65", " 6989.26", " 7408.08",
" 6696.87", " 6587.59", " 6674.26", " 5825.55", " 5889.98", " 6497.34",
" 6080.41", " 7512.29", " 7962.22", " 8413.40", " 7153.09", " 7460.55",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "2551.90", "2572.25", "2743.67", "2631.71",
"2778.78", "2457.02", "2519.70", "2556.61", "2259.24", "2377.52",
"2106.13", "2019.27", "1990.19", "1627.00", "2037.52", "1762.58",
"2209.05", "1773.60", "1975.03", "2474.83", "2515.84", "2796.33",
"2683.44", "2729.78", "2815.84", "2461.95", "2927.25", "2720.48",
"2827.54", "2561.22", "2706.59", "2480.40", "2436.47", "2475.69",
"2162.47", "2289.03", "2530.65", "2216.30", "2763.20", "2952.07",
"3078.76", "2656.94", "2770.48", "10162.76", "10271.75", "10989.96",
"10531.39", "11106.39", " 9847.93", "10067.73", "10243.22", " 9073.81",
" 9515.86", " 8374.93", " 8038.93", " 7971.49", " 6494.01", " 8086.93",
" 7011.61", " 8877.80", " 7118.37", " 7919.78", " 7596.31", " 6827.23",
" 7590.32", " 7301.27", " 7401.93", " 7639.75", " 6677.50", " 7947.16",
" 7399.59", " 7678.65", " 6989.26", " 7408.08", " 6696.87", " 6587.59",
" 6674.26", " 5825.55", " 5889.98", " 6497.34", " 6080.41", " 7512.29",
" 7962.22", " 8413.40", " 7153.09", " 7460.55", "7610.86", "7699.50",
"8246.29", "7899.68", "8327.61", "7390.91", "7548.03", "7686.61",
"6814.57", "7138.34", "6268.80", "6019.66", "5981.30", "4867.01",
"6049.41", "5249.03", "6668.75", "5344.77", "5944.75", "5121.48",
"4311.39", "4793.99", "4617.83", "4672.15", "4823.91", "4215.55",
"5019.91", "4679.11", "4851.11", "4428.04", "4701.49", "4216.47",
"4151.12", "4198.57", "3663.08", "3600.95", "3966.69", "3864.11",
"4749.09", "5010.15", "5334.64", "4496.15", "4690.07", "144.31",
"154.95", "158.53", "156.16", "158.48", "157.65", "159.09", "158.34",
"154.42", "153.09", "151.31", "154.02", "148.43", "147.72", "151.63",
"150.08", "163.18", "157.03", "160.16", "158.15", "156.58", "154.22",
"153.19", "151.64", "156.35", "158.48", "164.98", "161.17", "164.40",
"163.27", "167.59", "165.32", "166.08", "163.55", "158.14", "160.93",
"166.23", "169.05", "173.95", "169.72", "171.14", "159.12", "175.97",
"1270083.5", "1283577.5", "1373321.1", "1315784.8", "1387838.9",
"1230737.3", "1258276.1", "1280181.6", "1133781.8", "1188912.1",
"1046583.8", "1004802.2", " 996245.0", " 811598.4", "1010886.1",
" 876616.5", "1109601.4", " 889594.1", " 989943.9", " 949354.6",
" 853027.0", " 948310.1", " 912227.5", " 925007.2", " 954813.4",
" 834704.3", " 993865.2", " 925454.2", " 960578.5", " 874308.5",
" 926427.2", " 837038.2", " 823264.2", " 834267.9", " 728220.8",
" 736436.8", " 812370.7", " 760062.2", " 939356.5", " 995718.7",
"1051979.5", " 894250.4", " 932838.4"), .Dim = c(43L, 15L), .Dimnames = list(
NULL, c("DATA", "DATA2", "ANO", "MES", "QTDE_LOJA", "ID_ESTRELAS",
"QTDE_ID_ENDERECO", "QTDE_TRANSACAO", "QTDE_VL_COMISSAO",
"QTDE_VL_TAXA", "QTDE_VL_REEMBOLSO", "Receita_RPC", "Receita_Liquida",
"Ticket_Medio", "VL_TRANSACIONADO")))
Step by step from what I did, I started turning my data into an array and then turned that matrix into a time series.
dados = as.matrix(dados)
VL_TR_TS = ts(dados[,c("VL_TRANSACIONADO")], start = c(2016,1), end = c(2019,7), frequency = 12)
Then I created my training base, test and my model using auto..
treino = window(VL_TR_TS, start=c(2016,1), end=c(2019,2))
teste = window(VL_TR_TS, start=c(2019,3), end=c(2019,7))
modelo_1 = auto.arima(treino, seasonal = F)
But when I run the template script, it displays the above mentioned error message. In the field seasonal
left as FALSE
, why it was another error that does not indicate seasonality and I spent my base from 2 to 3 years because I saw in some forums that for the sample base to work in the auto.arima
would have to be more than 2 years, but still the error message continues.
What mistake am I making? What should I do to correct this error in the auto model.?
thanks for the reply, I will test.
– Izak Mandrak