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Forecasting output for all individual input in R
Plot of ACF & PACFInterpreting ACF and PACF plots for SARIMA modelR forecast.holtwinters in forecast package not foundExtract only the forecasted values from forecast()forecast::autolayer function produces an error with monthly time seriesForecast using ARIMA model in R environmentR's decompose/stl functions unable to completely extract annual seasonality from daily time seriesDoes the forecast function update the history (training) set with its new predictions?Specified forecast period not constraining forecast outputOut of sample forecasting issue with SARIMAX
0
I have a dataset with 3 different Item Numbers, with corresponding 36 months Quantity value. When I run the forecast output, it shows only the cumulative/only the first Item Numbers forecast. I want the output to show the forecast of each individual Item Number(That is Item1 - "004-0013" -> 6 months forecast, followed by Item2- "DP-023-0059"-> 6 months forecast and lastly Item3 -"502-00038R"-> 6 months forecast). Thanks in advance for any help.
p.s: This is my first post, so if there is an error in the format of the post, please do let me know.
library('ggplot2')
library('forecast')
library('tseries')
a <- read.csv("high.csv", stringsAsFactors = F)
a$Month <- as.Date(a$Month)
Qty_ts = ts(a[,c('Qty')])
a$Qty_ma12 = ma(a$Qty, order = 3)
Qty_ma = ts(na.omit(a$Qty_ma), start = c(2016,1),end = c(2019),frequency =
12)
decomp = stl(Qty_ma, s.window = "periodic")
deseasonal_Qty <- seasadj(decomp)
plot(decomp)
adf.test(Qty_ma, alternative = "stationary")
Acf(Qty_ma, main="")
Pacf(Qty_ma,main="")
Qty_d1 = diff(deseasonal_Qty, differences =1)
plot(Qty_d1)
adf.test(Qty_d1, alternative = "stationary")
Acf(Qty_d1, main ='ACF for differenced Series')
Pacf(Qty_d1,main ='PACF for Differenced Series')
auto.arima(deseasonal_Qty, seasonal = FALSE)
fit<- auto.arima(deseasonal_Qty, seasonal =FALSE)
tsdisplay(residuals(fit), lag.max=45, main='(0,1,1) Model Residuals')
fit2 = arima(deseasonal_Qty, order =c(1,1,0))
fit2
tsdisplay(residuals(fit2), lag.max=15, main='Seasonal model Residuals')
fcast<- forecast(fit2, h=6)
plot(fcast)
fcast
Results:
Item.Number Month Qty
1 004-0013 7/1/2017 1
2 DP-023-0059 12/1/2017 1
3 DP-023-0059 1/1/2018 1
4 502-00038R 11/1/2018 73
5 502-00038R 1/1/2019 738
6 502-00038R 6/1/2018 358
7 502-00038R 8/1/2018 751
8 502-00038R 5/1/2018 697
9 502-00038R 9/1/2018 1400
10 502-00038R 7/1/2018 210
11 004-0013 3/1/2018 4
12 004-0013 7/1/2016 4
13 502-00038R 12/1/2018 1832
14 DP-023-0059 12/1/2018 2
15 DP-023-0059 4/1/2017 2
16 DP-023-0059 11/1/2018 3
17 DP-023-0059 5/1/2016 3
18 502-00038R 5/1/2016 197
19 502-00038R 3/1/2018 302
20 502-00038R 2/1/2018 275
21 502-00038R 3/1/2017 291
22 502-00038R 3/1/2016 359
23 004-0013 8/1/2016 9
24 DP-023-0059 6/1/2017 4
25 DP-023-0059 11/1/2016 4
26 DP-023-0059 7/1/2017 4
27 DP-023-0059 9/1/2016 4
28 DP-023-0059 10/1/2017 4
29 DP-023-0059 5/1/2018 4
30 DP-023-0059 8/1/2016 4
31 DP-023-0059 6/1/2016 4
32 DP-023-0059 3/1/2018 4
33 DP-023-0059 2/1/2016 4
34 502-00038R 4/1/2017 365
35 502-00038R 1/1/2017 297
36 502-00038R 6/1/2016 590
37 502-00038R 7/1/2017 380
38 502-00038R 7/1/2016 418
39 502-00038R 10/1/2017 438
40 502-00038R 4/1/2018 288
41 502-00038R 5/1/2017 369
42 502-00038R 4/1/2016 237
43 DP-023-0059 3/1/2016 6
44 DP-023-0059 8/1/2017 5
45 DP-023-0059 2/1/2018 5
46 DP-023-0059 9/1/2017 5
47 DP-023-0059 8/1/2018 5
48 DP-023-0059 4/1/2016 5
49 DP-023-0059 6/1/2018 7
50 DP-023-0059 1/1/2016 6
51 DP-023-0059 7/1/2018 6
52 DP-023-0059 4/1/2018 6
53 DP-023-0059 11/1/2017 6
54 DP-023-0059 7/1/2016 6
55 DP-023-0059 1/1/2017 6
56 DP-023-0059 12/1/2016 6
57 502-00038R 1/1/2018 1483
58 502-00038R 2/1/2016 306
59 502-00038R 9/1/2016 420
60 502-00038R 8/1/2016 534
61 DP-023-0059 2/1/2017 9
62 DP-023-0059 5/1/2017 8
63 DP-023-0059 10/1/2018 8
64 502-00038R 11/1/2017 492
65 502-00038R 8/1/2017 723
66 502-00038R 12/1/2016 445
67 502-00038R 2/1/2017 1544
68 DP-023-0059 9/1/2018 9
69 502-00038R 1/1/2016 619
70 502-00038R 6/1/2017 679
71 502-00038R 9/1/2017 829
72 502-00038R 10/1/2016 517
73 DP-023-0059 3/1/2017 8
74 DP-023-0059 10/1/2016 8
75 502-00038R 12/1/2017 313
76 502-00038R 11/1/2016 867
77 004-0013 11/1/2018 14
78 004-0013 12/1/2018 15
79 004-0013 2/1/2017 14
80 004-0013 1/1/2019 19
81 004-0013 11/1/2016 30
82 004-0013 2/1/2018 13
83 004-0013 3/1/2017 9
84 004-0013 5/1/2017 4
85 004-0013 8/1/2017 15
86 004-0013 7/1/2018 13
87 004-0013 9/1/2016 16
88 004-0013 2/1/2016 17
89 004-0013 10/1/2018 6
90 004-0013 6/1/2018 7
91 004-0013 1/1/2018 6
92 004-0013 4/1/2017 11
93 004-0013 10/1/2017 17
94 004-0013 10/1/2016 21
95 004-0013 5/1/2018 13
96 004-0013 1/1/2017 12
97 004-0013 4/1/2016 24
98 004-0013 6/1/2017 11
99 004-0013 11/1/2017 12
100 004-0013 4/1/2018 14
101 004-0013 3/1/2016 13
102 004-0013 12/1/2016 12
103 004-0013 6/1/2016 16
104 004-0013 1/1/2016 6
105 004-0013 12/1/2017 9
106 004-0013 8/1/2018 12
107 004-0013 9/1/2017 21
108 004-0013 9/1/2018 6
109 004-0013 5/1/2016 12
r dplyr forecasting arima forecast
edited Mar 7 at 20:35
M-M
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
add a comment |
0
I have a dataset with 3 different Item Numbers, with corresponding 36 months Quantity value. When I run the forecast output, it shows only the cumulative/only the first Item Numbers forecast. I want the output to show the forecast of each individual Item Number(That is Item1 - "004-0013" -> 6 months forecast, followed by Item2- "DP-023-0059"-> 6 months forecast and lastly Item3 -"502-00038R"-> 6 months forecast). Thanks in advance for any help.
p.s: This is my first post, so if there is an error in the format of the post, please do let me know.
library('ggplot2')
library('forecast')
library('tseries')
a <- read.csv("high.csv", stringsAsFactors = F)
a$Month <- as.Date(a$Month)
Qty_ts = ts(a[,c('Qty')])
a$Qty_ma12 = ma(a$Qty, order = 3)
Qty_ma = ts(na.omit(a$Qty_ma), start = c(2016,1),end = c(2019),frequency =
12)
decomp = stl(Qty_ma, s.window = "periodic")
deseasonal_Qty <- seasadj(decomp)
plot(decomp)
adf.test(Qty_ma, alternative = "stationary")
Acf(Qty_ma, main="")
Pacf(Qty_ma,main="")
Qty_d1 = diff(deseasonal_Qty, differences =1)
plot(Qty_d1)
adf.test(Qty_d1, alternative = "stationary")
Acf(Qty_d1, main ='ACF for differenced Series')
Pacf(Qty_d1,main ='PACF for Differenced Series')
auto.arima(deseasonal_Qty, seasonal = FALSE)
fit<- auto.arima(deseasonal_Qty, seasonal =FALSE)
tsdisplay(residuals(fit), lag.max=45, main='(0,1,1) Model Residuals')
fit2 = arima(deseasonal_Qty, order =c(1,1,0))
fit2
tsdisplay(residuals(fit2), lag.max=15, main='Seasonal model Residuals')
fcast<- forecast(fit2, h=6)
plot(fcast)
fcast
Results:
Item.Number Month Qty
1 004-0013 7/1/2017 1
2 DP-023-0059 12/1/2017 1
3 DP-023-0059 1/1/2018 1
4 502-00038R 11/1/2018 73
5 502-00038R 1/1/2019 738
6 502-00038R 6/1/2018 358
7 502-00038R 8/1/2018 751
8 502-00038R 5/1/2018 697
9 502-00038R 9/1/2018 1400
10 502-00038R 7/1/2018 210
11 004-0013 3/1/2018 4
12 004-0013 7/1/2016 4
13 502-00038R 12/1/2018 1832
14 DP-023-0059 12/1/2018 2
15 DP-023-0059 4/1/2017 2
16 DP-023-0059 11/1/2018 3
17 DP-023-0059 5/1/2016 3
18 502-00038R 5/1/2016 197
19 502-00038R 3/1/2018 302
20 502-00038R 2/1/2018 275
21 502-00038R 3/1/2017 291
22 502-00038R 3/1/2016 359
23 004-0013 8/1/2016 9
24 DP-023-0059 6/1/2017 4
25 DP-023-0059 11/1/2016 4
26 DP-023-0059 7/1/2017 4
27 DP-023-0059 9/1/2016 4
28 DP-023-0059 10/1/2017 4
29 DP-023-0059 5/1/2018 4
30 DP-023-0059 8/1/2016 4
31 DP-023-0059 6/1/2016 4
32 DP-023-0059 3/1/2018 4
33 DP-023-0059 2/1/2016 4
34 502-00038R 4/1/2017 365
35 502-00038R 1/1/2017 297
36 502-00038R 6/1/2016 590
37 502-00038R 7/1/2017 380
38 502-00038R 7/1/2016 418
39 502-00038R 10/1/2017 438
40 502-00038R 4/1/2018 288
41 502-00038R 5/1/2017 369
42 502-00038R 4/1/2016 237
43 DP-023-0059 3/1/2016 6
44 DP-023-0059 8/1/2017 5
45 DP-023-0059 2/1/2018 5
46 DP-023-0059 9/1/2017 5
47 DP-023-0059 8/1/2018 5
48 DP-023-0059 4/1/2016 5
49 DP-023-0059 6/1/2018 7
50 DP-023-0059 1/1/2016 6
51 DP-023-0059 7/1/2018 6
52 DP-023-0059 4/1/2018 6
53 DP-023-0059 11/1/2017 6
54 DP-023-0059 7/1/2016 6
55 DP-023-0059 1/1/2017 6
56 DP-023-0059 12/1/2016 6
57 502-00038R 1/1/2018 1483
58 502-00038R 2/1/2016 306
59 502-00038R 9/1/2016 420
60 502-00038R 8/1/2016 534
61 DP-023-0059 2/1/2017 9
62 DP-023-0059 5/1/2017 8
63 DP-023-0059 10/1/2018 8
64 502-00038R 11/1/2017 492
65 502-00038R 8/1/2017 723
66 502-00038R 12/1/2016 445
67 502-00038R 2/1/2017 1544
68 DP-023-0059 9/1/2018 9
69 502-00038R 1/1/2016 619
70 502-00038R 6/1/2017 679
71 502-00038R 9/1/2017 829
72 502-00038R 10/1/2016 517
73 DP-023-0059 3/1/2017 8
74 DP-023-0059 10/1/2016 8
75 502-00038R 12/1/2017 313
76 502-00038R 11/1/2016 867
77 004-0013 11/1/2018 14
78 004-0013 12/1/2018 15
79 004-0013 2/1/2017 14
80 004-0013 1/1/2019 19
81 004-0013 11/1/2016 30
82 004-0013 2/1/2018 13
83 004-0013 3/1/2017 9
84 004-0013 5/1/2017 4
85 004-0013 8/1/2017 15
86 004-0013 7/1/2018 13
87 004-0013 9/1/2016 16
88 004-0013 2/1/2016 17
89 004-0013 10/1/2018 6
90 004-0013 6/1/2018 7
91 004-0013 1/1/2018 6
92 004-0013 4/1/2017 11
93 004-0013 10/1/2017 17
94 004-0013 10/1/2016 21
95 004-0013 5/1/2018 13
96 004-0013 1/1/2017 12
97 004-0013 4/1/2016 24
98 004-0013 6/1/2017 11
99 004-0013 11/1/2017 12
100 004-0013 4/1/2018 14
101 004-0013 3/1/2016 13
102 004-0013 12/1/2016 12
103 004-0013 6/1/2016 16
104 004-0013 1/1/2016 6
105 004-0013 12/1/2017 9
106 004-0013 8/1/2018 12
107 004-0013 9/1/2017 21
108 004-0013 9/1/2018 6
109 004-0013 5/1/2016 12
r dplyr forecasting arima forecast
edited Mar 7 at 20:35
M-M
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
add a comment |
0
0
0
I have a dataset with 3 different Item Numbers, with corresponding 36 months Quantity value. When I run the forecast output, it shows only the cumulative/only the first Item Numbers forecast. I want the output to show the forecast of each individual Item Number(That is Item1 - "004-0013" -> 6 months forecast, followed by Item2- "DP-023-0059"-> 6 months forecast and lastly Item3 -"502-00038R"-> 6 months forecast). Thanks in advance for any help.
p.s: This is my first post, so if there is an error in the format of the post, please do let me know.
library('ggplot2')
library('forecast')
library('tseries')
a <- read.csv("high.csv", stringsAsFactors = F)
a$Month <- as.Date(a$Month)
Qty_ts = ts(a[,c('Qty')])
a$Qty_ma12 = ma(a$Qty, order = 3)
Qty_ma = ts(na.omit(a$Qty_ma), start = c(2016,1),end = c(2019),frequency =
12)
decomp = stl(Qty_ma, s.window = "periodic")
deseasonal_Qty <- seasadj(decomp)
plot(decomp)
adf.test(Qty_ma, alternative = "stationary")
Acf(Qty_ma, main="")
Pacf(Qty_ma,main="")
Qty_d1 = diff(deseasonal_Qty, differences =1)
plot(Qty_d1)
adf.test(Qty_d1, alternative = "stationary")
Acf(Qty_d1, main ='ACF for differenced Series')
Pacf(Qty_d1,main ='PACF for Differenced Series')
auto.arima(deseasonal_Qty, seasonal = FALSE)
fit<- auto.arima(deseasonal_Qty, seasonal =FALSE)
tsdisplay(residuals(fit), lag.max=45, main='(0,1,1) Model Residuals')
fit2 = arima(deseasonal_Qty, order =c(1,1,0))
fit2
tsdisplay(residuals(fit2), lag.max=15, main='Seasonal model Residuals')
fcast<- forecast(fit2, h=6)
plot(fcast)
fcast
Results:
Item.Number Month Qty
1 004-0013 7/1/2017 1
2 DP-023-0059 12/1/2017 1
3 DP-023-0059 1/1/2018 1
4 502-00038R 11/1/2018 73
5 502-00038R 1/1/2019 738
6 502-00038R 6/1/2018 358
7 502-00038R 8/1/2018 751
8 502-00038R 5/1/2018 697
9 502-00038R 9/1/2018 1400
10 502-00038R 7/1/2018 210
11 004-0013 3/1/2018 4
12 004-0013 7/1/2016 4
13 502-00038R 12/1/2018 1832
14 DP-023-0059 12/1/2018 2
15 DP-023-0059 4/1/2017 2
16 DP-023-0059 11/1/2018 3
17 DP-023-0059 5/1/2016 3
18 502-00038R 5/1/2016 197
19 502-00038R 3/1/2018 302
20 502-00038R 2/1/2018 275
21 502-00038R 3/1/2017 291
22 502-00038R 3/1/2016 359
23 004-0013 8/1/2016 9
24 DP-023-0059 6/1/2017 4
25 DP-023-0059 11/1/2016 4
26 DP-023-0059 7/1/2017 4
27 DP-023-0059 9/1/2016 4
28 DP-023-0059 10/1/2017 4
29 DP-023-0059 5/1/2018 4
30 DP-023-0059 8/1/2016 4
31 DP-023-0059 6/1/2016 4
32 DP-023-0059 3/1/2018 4
33 DP-023-0059 2/1/2016 4
34 502-00038R 4/1/2017 365
35 502-00038R 1/1/2017 297
36 502-00038R 6/1/2016 590
37 502-00038R 7/1/2017 380
38 502-00038R 7/1/2016 418
39 502-00038R 10/1/2017 438
40 502-00038R 4/1/2018 288
41 502-00038R 5/1/2017 369
42 502-00038R 4/1/2016 237
43 DP-023-0059 3/1/2016 6
44 DP-023-0059 8/1/2017 5
45 DP-023-0059 2/1/2018 5
46 DP-023-0059 9/1/2017 5
47 DP-023-0059 8/1/2018 5
48 DP-023-0059 4/1/2016 5
49 DP-023-0059 6/1/2018 7
50 DP-023-0059 1/1/2016 6
51 DP-023-0059 7/1/2018 6
52 DP-023-0059 4/1/2018 6
53 DP-023-0059 11/1/2017 6
54 DP-023-0059 7/1/2016 6
55 DP-023-0059 1/1/2017 6
56 DP-023-0059 12/1/2016 6
57 502-00038R 1/1/2018 1483
58 502-00038R 2/1/2016 306
59 502-00038R 9/1/2016 420
60 502-00038R 8/1/2016 534
61 DP-023-0059 2/1/2017 9
62 DP-023-0059 5/1/2017 8
63 DP-023-0059 10/1/2018 8
64 502-00038R 11/1/2017 492
65 502-00038R 8/1/2017 723
66 502-00038R 12/1/2016 445
67 502-00038R 2/1/2017 1544
68 DP-023-0059 9/1/2018 9
69 502-00038R 1/1/2016 619
70 502-00038R 6/1/2017 679
71 502-00038R 9/1/2017 829
72 502-00038R 10/1/2016 517
73 DP-023-0059 3/1/2017 8
74 DP-023-0059 10/1/2016 8
75 502-00038R 12/1/2017 313
76 502-00038R 11/1/2016 867
77 004-0013 11/1/2018 14
78 004-0013 12/1/2018 15
79 004-0013 2/1/2017 14
80 004-0013 1/1/2019 19
81 004-0013 11/1/2016 30
82 004-0013 2/1/2018 13
83 004-0013 3/1/2017 9
84 004-0013 5/1/2017 4
85 004-0013 8/1/2017 15
86 004-0013 7/1/2018 13
87 004-0013 9/1/2016 16
88 004-0013 2/1/2016 17
89 004-0013 10/1/2018 6
90 004-0013 6/1/2018 7
91 004-0013 1/1/2018 6
92 004-0013 4/1/2017 11
93 004-0013 10/1/2017 17
94 004-0013 10/1/2016 21
95 004-0013 5/1/2018 13
96 004-0013 1/1/2017 12
97 004-0013 4/1/2016 24
98 004-0013 6/1/2017 11
99 004-0013 11/1/2017 12
100 004-0013 4/1/2018 14
101 004-0013 3/1/2016 13
102 004-0013 12/1/2016 12
103 004-0013 6/1/2016 16
104 004-0013 1/1/2016 6
105 004-0013 12/1/2017 9
106 004-0013 8/1/2018 12
107 004-0013 9/1/2017 21
108 004-0013 9/1/2018 6
109 004-0013 5/1/2016 12
r dplyr forecasting arima forecast
edited Mar 7 at 20:35
M-M
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
I have a dataset with 3 different Item Numbers, with corresponding 36 months Quantity value. When I run the forecast output, it shows only the cumulative/only the first Item Numbers forecast. I want the output to show the forecast of each individual Item Number(That is Item1 - "004-0013" -> 6 months forecast, followed by Item2- "DP-023-0059"-> 6 months forecast and lastly Item3 -"502-00038R"-> 6 months forecast). Thanks in advance for any help.
p.s: This is my first post, so if there is an error in the format of the post, please do let me know.
library('ggplot2')
library('forecast')
library('tseries')
a <- read.csv("high.csv", stringsAsFactors = F)
a$Month <- as.Date(a$Month)
Qty_ts = ts(a[,c('Qty')])
a$Qty_ma12 = ma(a$Qty, order = 3)
Qty_ma = ts(na.omit(a$Qty_ma), start = c(2016,1),end = c(2019),frequency =
12)
decomp = stl(Qty_ma, s.window = "periodic")
deseasonal_Qty <- seasadj(decomp)
plot(decomp)
adf.test(Qty_ma, alternative = "stationary")
Acf(Qty_ma, main="")
Pacf(Qty_ma,main="")
Qty_d1 = diff(deseasonal_Qty, differences =1)
plot(Qty_d1)
adf.test(Qty_d1, alternative = "stationary")
Acf(Qty_d1, main ='ACF for differenced Series')
Pacf(Qty_d1,main ='PACF for Differenced Series')
auto.arima(deseasonal_Qty, seasonal = FALSE)
fit<- auto.arima(deseasonal_Qty, seasonal =FALSE)
tsdisplay(residuals(fit), lag.max=45, main='(0,1,1) Model Residuals')
fit2 = arima(deseasonal_Qty, order =c(1,1,0))
fit2
tsdisplay(residuals(fit2), lag.max=15, main='Seasonal model Residuals')
fcast<- forecast(fit2, h=6)
plot(fcast)
fcast
Results:
Item.Number Month Qty
1 004-0013 7/1/2017 1
2 DP-023-0059 12/1/2017 1
3 DP-023-0059 1/1/2018 1
4 502-00038R 11/1/2018 73
5 502-00038R 1/1/2019 738
6 502-00038R 6/1/2018 358
7 502-00038R 8/1/2018 751
8 502-00038R 5/1/2018 697
9 502-00038R 9/1/2018 1400
10 502-00038R 7/1/2018 210
11 004-0013 3/1/2018 4
12 004-0013 7/1/2016 4
13 502-00038R 12/1/2018 1832
14 DP-023-0059 12/1/2018 2
15 DP-023-0059 4/1/2017 2
16 DP-023-0059 11/1/2018 3
17 DP-023-0059 5/1/2016 3
18 502-00038R 5/1/2016 197
19 502-00038R 3/1/2018 302
20 502-00038R 2/1/2018 275
21 502-00038R 3/1/2017 291
22 502-00038R 3/1/2016 359
23 004-0013 8/1/2016 9
24 DP-023-0059 6/1/2017 4
25 DP-023-0059 11/1/2016 4
26 DP-023-0059 7/1/2017 4
27 DP-023-0059 9/1/2016 4
28 DP-023-0059 10/1/2017 4
29 DP-023-0059 5/1/2018 4
30 DP-023-0059 8/1/2016 4
31 DP-023-0059 6/1/2016 4
32 DP-023-0059 3/1/2018 4
33 DP-023-0059 2/1/2016 4
34 502-00038R 4/1/2017 365
35 502-00038R 1/1/2017 297
36 502-00038R 6/1/2016 590
37 502-00038R 7/1/2017 380
38 502-00038R 7/1/2016 418
39 502-00038R 10/1/2017 438
40 502-00038R 4/1/2018 288
41 502-00038R 5/1/2017 369
42 502-00038R 4/1/2016 237
43 DP-023-0059 3/1/2016 6
44 DP-023-0059 8/1/2017 5
45 DP-023-0059 2/1/2018 5
46 DP-023-0059 9/1/2017 5
47 DP-023-0059 8/1/2018 5
48 DP-023-0059 4/1/2016 5
49 DP-023-0059 6/1/2018 7
50 DP-023-0059 1/1/2016 6
51 DP-023-0059 7/1/2018 6
52 DP-023-0059 4/1/2018 6
53 DP-023-0059 11/1/2017 6
54 DP-023-0059 7/1/2016 6
55 DP-023-0059 1/1/2017 6
56 DP-023-0059 12/1/2016 6
57 502-00038R 1/1/2018 1483
58 502-00038R 2/1/2016 306
59 502-00038R 9/1/2016 420
60 502-00038R 8/1/2016 534
61 DP-023-0059 2/1/2017 9
62 DP-023-0059 5/1/2017 8
63 DP-023-0059 10/1/2018 8
64 502-00038R 11/1/2017 492
65 502-00038R 8/1/2017 723
66 502-00038R 12/1/2016 445
67 502-00038R 2/1/2017 1544
68 DP-023-0059 9/1/2018 9
69 502-00038R 1/1/2016 619
70 502-00038R 6/1/2017 679
71 502-00038R 9/1/2017 829
72 502-00038R 10/1/2016 517
73 DP-023-0059 3/1/2017 8
74 DP-023-0059 10/1/2016 8
75 502-00038R 12/1/2017 313
76 502-00038R 11/1/2016 867
77 004-0013 11/1/2018 14
78 004-0013 12/1/2018 15
79 004-0013 2/1/2017 14
80 004-0013 1/1/2019 19
81 004-0013 11/1/2016 30
82 004-0013 2/1/2018 13
83 004-0013 3/1/2017 9
84 004-0013 5/1/2017 4
85 004-0013 8/1/2017 15
86 004-0013 7/1/2018 13
87 004-0013 9/1/2016 16
88 004-0013 2/1/2016 17
89 004-0013 10/1/2018 6
90 004-0013 6/1/2018 7
91 004-0013 1/1/2018 6
92 004-0013 4/1/2017 11
93 004-0013 10/1/2017 17
94 004-0013 10/1/2016 21
95 004-0013 5/1/2018 13
96 004-0013 1/1/2017 12
97 004-0013 4/1/2016 24
98 004-0013 6/1/2017 11
99 004-0013 11/1/2017 12
100 004-0013 4/1/2018 14
101 004-0013 3/1/2016 13
102 004-0013 12/1/2016 12
103 004-0013 6/1/2016 16
104 004-0013 1/1/2016 6
105 004-0013 12/1/2017 9
106 004-0013 8/1/2018 12
107 004-0013 9/1/2017 21
108 004-0013 9/1/2018 6
109 004-0013 5/1/2016 12
r dplyr forecasting arima forecast
r dplyr forecasting arima forecast
edited Mar 7 at 20:35
M-M
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
edited Mar 7 at 20:35
M-M
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
edited Mar 7 at 20:35
M-M
7,01462046
edited Mar 7 at 20:35
M-M
7,01462046
edited Mar 7 at 20:35
M-M
7,01462046
7,01462046
asked Mar 7 at 20:30
kk1791kk1791
14
asked Mar 7 at 20:30
kk1791kk1791
14
asked Mar 7 at 20:30
kk1791kk1791
14
14
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