Dear Jim,
Thank you for your valuable replies. So variable mf5 has the forecasts for
a fitted nnetar model.
Below is the class and the str(mf5) output. Maybe with this information I
am sharing with you, hopefully you can give me some more guidance.
Again, thank you so much!
> class(mf5)
[1] "forecast"
>
> str(mf5)
List of 16
$ x : Time-Series [1:377] from 1986 to 2017: 48 40 44 35 44 42 39
37 41 36 ...
$ m : num 12
$ p : num 25
$ P : num 1
$ scalex :List of 2
..$ center: num 38.1
..$ scale : num 10.9
$ size : num 13
$ subset : int [1:377] 1 2 3 4 5 6 7 8 9 10 ...
$ model :List of 20
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 5.83
.. ..$ wts : num [1:352] -1.081 2.199 0.734 1.358 1.115 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.246 0.439 -0.357 1.325 -0.195 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.0754 0.00853 -0.0242 -0.05026
0.0898 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.54
.. ..$ wts : num [1:352] -7.113 4.668 -0.801 -5.487 2.751 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.145 0.443 -0.401 1.286 -0.158 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.0258 0.0037 0.02 -0.0107 0.0528 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 1.1
.. ..$ wts : num [1:352] -2.24 -0.698 -0.243 -2.575 4.073 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.2466 0.2986 -0.4369 1.3053 -0.0965
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.07551 0.14845 0.056 -0.03027
-0.00844 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 1.8
.. ..$ wts : num [1:352] 0.427 1.89 -0.705 -3.121 -1.134 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.11 0.37 -0.38 1.25 -0.15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.06113 0.07689 -0.00135 0.02279
0.04491 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.67
.. ..$ wts : num [1:352] -0.632 2.121 6.144 1.697 0.953 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.2228 0.4483 -0.3944 1.236 -0.0856
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.05171 -0.00123 0.01351 0.03908
-0.01928 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 1.8
.. ..$ wts : num [1:352] -0.919 -0.769 1.105 -0.444 4.516 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.0833 0.4158 -0.3748 1.3173 -0.082
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.08773 0.03123 -0.00616 -0.04231
-0.02296 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.65
.. ..$ wts : num [1:352] 2.03 -1.6 -1.91 -4.65 -3.03 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.199 0.447 -0.443 1.263 -0.138 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.02808 0.000236 0.061884 0.012432
0.033544 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 7.79
.. ..$ wts : num [1:352] -3.314 3.89 -6.355 0.842 4.491 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.171 0.462 -0.337 1.24 -0.128 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.000172 -0.014804 -0.044404 0.03475
0.023508 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.3
.. ..$ wts : num [1:352] 5.12 -2.27 2.25 4.02 -6.78 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.1137 0.4326 -0.4635 1.2762 -0.0999
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.05741 0.01451 0.08259 -0.00115
-0.00503 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.42
.. ..$ wts : num [1:352] 4.12 -1.78 2.89 4.28 2.57 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.0986 0.4599 -0.4425 1.2061 -0.1931
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.0724 -0.0128 0.0615 0.069 0.0882
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.81
.. ..$ wts : num [1:352] -1.207 -1.817 -3.409 -4.643 0.775 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.0905 0.5451 -0.4395 1.1706 -0.0989
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.08053 -0.09806 0.05853 0.10442
-0.00604 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 6.06
.. ..$ wts : num [1:352] 1.092 4.676 -0.514 1.13 1.276 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.1781 0.4221 -0.4852 1.2227 -0.0774
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.007 0.025 0.1043 0.0523 -0.0275
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 4.1
.. ..$ wts : num [1:352] -4.1 1.95 -5.39 6.06 -3.65 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.1674 0.4017 -0.3968 1.2474 -0.0835
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.00363 0.0454 0.01586 0.02764
-0.02148 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 1.22
.. ..$ wts : num [1:352] -2.5 1.43 2.96 -2.31 -1.18 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.1436 0.446 -0.4246 1.2081 -0.0853
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.02742 0.00101 0.04371 0.06697
-0.01964 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 0.858
.. ..$ wts : num [1:352] 2.6535 4.398 0.0444 4.9804 -0.6771 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.089 0.4388 -0.283 1.1308 -0.0592
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.0821 0.0083 -0.0979 0.1443 -0.0457
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.19
.. ..$ wts : num [1:352] 1.216 -4.28 -2.531 0.987 1.311 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.1548 0.4733 -0.3074 1.2065 -0.0909
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.0163 -0.0263 -0.0735 0.0685 -0.014
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.51
.. ..$ wts : num [1:352] -0.862 1.953 1.304 -0.273 1.033 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.141 0.492 -0.376 1.244 -0.101 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.02978 -0.04529 -0.00509 0.03066
-0.00362 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.02
.. ..$ wts : num [1:352] 6.78 -0.31 -1.04 -7.21 2.37 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.0302 0.4157 -0.3828 1.134 -0.1247
...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.14087 0.03136 0.00183 0.14107
0.01977 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 1.81
.. ..$ wts : num [1:352] 0.385 1.215 -2.217 -1.288 -1.172 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] 0.26023 0.47306 -0.31769 1.29285
-0.00974 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] -0.0892 -0.026 -0.0632 -0.0178
-0.0952 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..$ :List of 15
.. ..$ n : num [1:3] 25 13 1
.. ..$ nunits : int 40
.. ..$ nconn : num [1:41] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ conn : num [1:352] 0 1 2 3 4 5 6 7 8 9 ...
.. ..$ nsunits : num 39
.. ..$ decay : num 0
.. ..$ entropy : logi FALSE
.. ..$ softmax : logi FALSE
.. ..$ censored : logi FALSE
.. ..$ value : num 2.11
.. ..$ wts : num [1:352] 3.38 -2.75 -3.5 -1.13 -8.48 ...
.. ..$ convergence : int 1
.. ..$ fitted.values: num [1:352, 1] -0.0863 0.4472 -0.3007 1.2226
-0.1176 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ residuals : num [1:352, 1] 0.257314 -0.000178 -0.080274
0.052434 0.012663 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : NULL
.. .. .. ..$ : NULL
.. ..$ call : language nnet.default(x = x, y = y, size = ..1,
linout = linout, trace = trace)
.. ..- attr(*, "class")= chr "nnet"
..- attr(*, "class")= chr "nnetarmodels"
$ nnetargs : list()
$ fitted : Time-Series [1:377] from 1986 to 2017: NA NA NA NA NA NA NA
NA NA NA ...
$ residuals: Time-Series [1:377] from 1986 to 2017: NA NA NA NA NA NA NA
NA NA NA ...
$ lags : num [1:25] 1 2 3 4 5 6 7 8 9 10 ...
$ series : chr "TSDat"
$ method : chr "NNAR(25,1,13)[12]"
$ call : language nnetar(y = TSDat)
$ mean : Time-Series [1:24] from 2017 to 2019: 54.7 50.3 55.4 62.6
59.7 ...
- attr(*, "class")= chr "forecast"
>
2017-03-17 15:48 GMT-05:00 Jim Lemon <drjimle...@gmail.com>:
> Hi Paul,
> When manipulating any R object, the first thing to ascertain is what it is:
>
> class(TSmodelForecast)
>
> should give you useful information.
>
> str(TSmodelForecast)
>
> should give you more. Because of the wealth of defined data structures
> in R, it is difficult to manipulate them without this information. I
> suspect that your output is something like a time series object, and
> once that is known, it should not be too hard to display its contents
> in the way you want.
>
> Jim
>
>
> On Sat, Mar 18, 2017 at 7:12 AM, Paul Bernal <paulberna...@gmail.com>
> wrote:
> > Dear Jim,
> >
> > Hope you are doing great. I tried to do what you suggested but R send an
> > error message saying that $ operator is invalid for atomic vectors.
> >
> > The format of the forecasts are as follows: forecasted years are as rows,
> > and forecasted months are in columns what I want to do is to have two
> > colums, one with the forecasted dates in (MMM-YYYY format) and the second
> > column with the actual forecast results.
> >
> > The output that is giving me hard time is the forecast output from nnetar
> > model.
> >
> > Thanks for your valuable support,
> >
> > Best of regards,
> >
> > Paul
> >
> >
> > 2017-03-16 18:23 GMT-05:00 Jim Lemon <drjimle...@gmail.com>:
> >>
> >> Hi Paul,
> >> It looks like the information that is printed is in:
> >>
> >> TSModelForecast$mean
> >>
> >> If str(TSModelForecast$mean) returns something like a list with two
> >> components, you can probably use something like this:
> >>
> >> paste(format(TSModelForecast$mean$Date,"%b-%Y"),
> >> TSModelForecast$mean$Forecast,sep="-",collapse="\n")
> >>
> >> It also might be in TSModelForecast$fitted
> >>
> >> Jim
> >>
> >>
> >> On Fri, Mar 17, 2017 at 5:34 AM, Paul Bernal <paulberna...@gmail.com>
> >> wrote:
> >> > Dear friends,
> >> >
> >> > I am currently using R version 3.3.3 (64-bit) and used the following
> >> > code
> >> > to generate forecasts:
> >> >
> >> >> library(forecast)
> >> >>
> >> >> library(tseries)
> >> >
> >> > ‘tseries’ version: 0.10-35
> >> >
> >> > ‘tseries’ is a package for time series analysis and computational
> >> > finance.
> >> >
> >> > See ‘library(help="tseries")’ for details.
> >> >
> >> >
> >> >> DAT<-read.csv("TrainingData.csv")
> >> >>
> >> >> TSdata<-ts(DAT[,1], start=c(1994,10), frequency=12)
> >> >>
> >> >> TSmodel<-nnetar(TSdata)
> >> >>
> >> >> TSmodelForecast<-forecast(TSmodel, h=24)
> >> >>
> >> >> TSmodelForecast
> >> >
> >> > The problem is that the output comes in this fashion:
> >> >
> >> > Jan Feb Mar Apr May Jun Jul Aug
> >> > Sep Oct
> >> > 2017 10 20 15 40 9 8 21
> >> > 21
> >> > 19 18
> >> > 2018 34 15 7 6 10 11
> >> >
> >> > The format I would like to have is the following:
> >> >
> >> > Date Forecast
> >> > Jan-2017 10
> >> > Feb-2017 20
> >> > Mar-2017 15
> >> > Apr-2017 40
> >> > May-2017 9
> >> > Jun-2017 8
> >> > Jul-2017 21
> >> > Aug-2017 21
> >> > Sep-2017 19
> >> > etc etc
> >> >
> >> > Is there a way to make the results look like this?
> >> >
> >> > Attached is a dataset as a reference.
> >> >
> >> > Best regards,
> >> >
> >> > Paul
> >> > ______________________________________________
> >> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >> > https://stat.ethz.ch/mailman/listinfo/r-help
> >> > PLEASE do read the posting guide
> >> > http://www.R-project.org/posting-guide.html
> >> > and provide commented, minimal, self-contained, reproducible code.
> >
> >
>
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