[R] [FORGED] Fitting arima Models with Exogenous Variables
Rolf Turner
r.turner at auckland.ac.nz
Thu Jan 26 20:49:41 CET 2017
I am re-sending this since I have been told that the attachments that I
made did not get through. So I am trying again with *.dput attachments.
You will need to read them in using dget().
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
On 24/01/17 10:35, Rolf Turner wrote:
>
> This should have been sent to the R-help mailing list, not to me
> personally. I am not an expert on this sort of time series modelling
> and cannot thereby provide any useful advice. My reply to you was of a
> "generic" nature --- when making an enquiry, provide a reproducible
> example!!!
>
> I am cc-ing this email to the R-help list, since someone on that list
> *may* be able to answer your question. I have (re-) attached the data
> sets that you sent to me.
>
> cheers,
>
> Rolf Turner
>
> On 24/01/17 04:36, Paul Bernal wrote:
>> Hello Rolf,
>>
>> Thank you for your kind reply. I am attaching two datasets, one with the
>> historical data that I used to train the model, and the other one with
>> the exogenous variables.
>>
>> The R code that I used is as follows:
>>
>>> library(forecast)
>>> library(tseries)
>>> library(TSA)
>>> library(stats)
>>> library(stats4)
>>> TrainingDat<-read.csv("Training Data.csv")
>>>
>>> ExogVars<-read.csv("ExogenousVariables5.csv")
>>> #The file ExogVars contains 5 columns, one column for each regressor
>>> Model1<-auto.arima(TrainingDat[,5], xreg=ExogVars)
>>> #In Model1 I was able to incorporate xreg without any trouble
>>> #The problem comes when trying to incorporate newxreg
>>> Model2<-auto.arima(ExoVars[1:5])
>> Error in as.ts(x) : object 'ExoVars' not found
>>>
>>> Model2<-auto.arima(ExogVars[1:5])
>>
>> Error in auto.arima(ExogVars[1:5]) : No suitable ARIMA model found
>>>
>>> Model2<-auto.arima(ExogVars[,1])
>>>
>>> NewXReg<-forecast(Model2, h=12)
>>>
>>> Forec<-forecast(Model1, newxreg=NewXReg)
>> Error in forecast.Arima(Model1, newxreg = NewXReg) :
>> No regressors provided
>> In addition: Warning message:
>> In forecast.Arima(Model1, newxreg = NewXReg) :
>> The non-existent newxreg arguments will be ignored.
>>>
>>> Forec<-forecast(Model1, newxreg=NewXReg$mean)
>> Error in forecast.Arima(Model1, newxreg = NewXReg$mean) :
>> No regressors provided
>> In addition: Warning message:
>> In forecast.Arima(Model1, newxreg = NewXReg$mean) :
>> The non-existent newxreg arguments will be ignored.
>>
>> I would like to generate the forecasts for all 4 variables included in
>> the Training set, along with all 5 regressors, but it seems like I can
>> only chose one training variable at a time, and one regressor at a time.
>>
>> Please let me know if you can work this out,
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-------------- next part --------------
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147.63, 147.25, 164.8, 177.13, 163.63, 131.5, 126.5, 126.63,
129.9, 135.88, 146.38, 132, 127.25, 137.3, 149.13, 126.25, 106.1,
112.5, 109.63, 119.38, 129.9, 144.25, 151.1, 149.13, 153, 160.7,
172.25, 162.13, 144, 155.38, 176.8, 200.13, 176.63, 148.88, 159.3,
162.88, 173.88, 166.8, 156.75, 159.4, 158.25, 156.25, 162.3,
169.63, 163.13, 172.9, 187.75, 179.5, 179.6, 185.13, 178, 193.1,
177.75, 171.8, 184.63, 196.88, 210.63, 247.6, 256.88, 257.75,
264.9, 275, 312.6, 301.38, 293.25, 277.6, 297, 319.25, 330.4,
345.75, 342.88, 323.8, 337, 326.5, 282.9, 282.5, 272, 270.6,
264.25, 302, 314.8, 343.63, 344, 349.5, 379.25, 381.3, 404.38,
428.75, 500.6, 472.5, 484.13, 476.6, 507.38, 531.38, 582.35,
628.5, 715.25, 703.6, 595, 418.7, 242.63, 226.75, 254.6, 258.75,
243.88, 284.13, 339.8, 406.75, 406.8, 454.38, 456.75, 441.5,
466.75, 459.25, 481.8, 465.25, 472.25, 481.2, 460.38, 443.25,
442.2, 453.5, 445.5, 473.1, 492.63, 507, 543.5, 631.5, 638.5,
672.7, 651, 659, 674.5, 671.5, 661.9, 659.75, 690.5, 680.8, 725.75,
731.88, 745.2, 729.13, 683.63, 604.4, 620.38, 668.2, 665.25,
638.5, 604.8, 605, 619.5, 653.5, 637.4, 617.25, 608.8, 614.5,
593.13, 605.8, 608.25, 616.75, 618.9, 607.75, 617.4, 605.5, 601.75,
596, 596.5, 608.63, 602.75, 602.5, 590.63, 505.9, 454.88, 358.75,
295.3, 354.75, 335.63, 337.88, 379.8, 348.63, 311.8, 250.25,
238.88, 245.7, 226.25, 178.75), X380CSTRotterdam = c(89.13, 102.88,
96.1, 108.5, 103.38, 106.2, 105.19, 107.88, 93.6, 81.38, 83.25,
87.5, 85.88, 85.5, 102.8, 103.63, 95.5, 106.6, 115.38, 99.6,
88.63, 88.13, 97.4, 114.5, 122.5, 116.5, 120.38, 110.2, 96.38,
89.75, 86.13, 84.6, 86.63, 89.38, 108.69, 97.5, 104.6, 107.5,
88.69, 73.5, 69, 67.25, 79.75, 73.2, 67.38, 66.7, 62.63, 66.25,
69.5, 60.5, 55.8, 64.5, 57.75, 61.88, 72.8, 71.88, 80.75, 95.7,
112.88, 117.75, 127.7, 130, 127.3, 127.5, 133.88, 147.3, 125.5,
126.5, 144.3, 132.88, 132.5, 154.5, 159.88, 149.75, 126.7, 117.9,
121.5, 119.8, 116.75, 122, 121.6, 118, 125.4, 130.13, 110.63,
101.9, 103.75, 104.5, 103.25, 120.5, 136.13, 140.8, 135, 142.75,
145.9, 161.38, 154, 125.7, 134.38, 173.5, 175.13, 148.25, 126.38,
137.9, 147.5, 170.5, 159.7, 147.75, 151.9, 154.75, 141, 141.4,
139, 144.5, 152.6, 170.63, 159.5, 162.9, 167, 161.75, 173.9,
146.5, 143.5, 157.5, 171.25, 203, 231.9, 230.13, 232.25, 248.9,
261.75, 288.4, 270.5, 256.38, 255.8, 282.88, 294.75, 300.3, 320.13,
324.63, 301.4, 316.5, 311, 280.5, 266.13, 262.38, 255.9, 229.13,
251.5, 272.7, 312, 325.88, 325.9, 359.88, 353.7, 374, 412.5,
476.1, 447.5, 447.75, 436.9, 477.38, 494.88, 542.9, 593.76, 679.5,
635.6, 544.13, 398, 217.63, 194.5, 225.7, 239.13, 244.5, 276.25,
327.5, 384.25, 382.8, 429.38, 412.38, 423.1, 462, 438.75, 457.2,
445.5, 453.25, 467, 437.38, 424.38, 423.7, 439.63, 431.5, 458.6,
476, 488.6, 514.75, 575.25, 606.38, 641.6, 621.75, 631.25, 647.5,
634.38, 640.5, 633, 645.13, 623.8, 682.38, 694.63, 712.6, 697,
651.5, 572.4, 597.13, 640.5, 640, 616.75, 588, 582.75, 608.75,
634.5, 605.6, 584, 580.2, 580, 596.75, 601.6, 597.5, 588.5, 575.9,
584.25, 570.4, 580, 574.5, 578.75, 576.4, 590.25, 575, 563.4,
547.5, 481.6, 420, 327.88, 250.1, 304.63, 301.88, 307.25, 343.7,
328.5, 292.7, 233.75, 226.63, 222.5, 201, 157.13), X380CSTHouston = c(91,
94.25, 89.2, 90, 96.75, 97.05, 100.19, 106.75, 101.5, 85.5, 83.5,
84.7, 86.5, 86.75, 99.2, 99.75, 99.13, 102.7, 107.38, 101.4,
96.75, 93.5, 96.1, 111.38, 130.3, 119.75, 112, 106.3, 90.5, 90.75,
93.38, 96.4, 94.88, 95.38, 107.55, 99.63, 109, 108.63, 86.88,
74.4, 72.75, 59, 78.13, 72.1, 67.88, 68.4, 62.63, 65.13, 68.9,
63.38, 53.3, 56.13, 52, 63, 82.2, 81, 81.88, 92.2, 114.5, 119.13,
125.5, 126.5, 125.8, 128, 135.63, 141.1, 123.75, 132, 145.6,
133.25, 128.88, 148.5, 152.88, 142, 120.1, 116.4, 129.5, 120.9,
104.13, 114.38, 111.1, 113.5, 117, 128.75, 105.75, 93.8, 98.38,
99.5, 97.75, 117.9, 139.25, 142, 139, 141.75, 148.6, 158.88,
153, 128.4, 142.5, 195, 186.25, 160.25, 134.88, 139.9, 152, 167.75,
172.1, 149.13, 157.7, 158, 150, 148.9, 153.5, 151.13, 160.1,
181.13, 167.5, 164.5, 170.38, 171.13, 213.3, 152.88, 173.4, 182.63,
177, 200.25, 247, 255.13, 253.63, 253.5, 264.63, 305.3, 294.63,
269.13, 276.4, 300, 310.25, 309.4, 330.68, 334.13, 309.4, 326.13,
331.38, 284.9, 269, 263.38, 267.5, 247.75, 267.25, 272.7, 305.75,
334.75, 348.3, 359.88, 366.4, 376.38, 403.25, 482, 457, 458.5,
454.6, 486.13, 498.13, 568.4, 629.5, 721.25, 661.5, 592, 420.5,
242, 229.5, 256.9, 265.13, 247.38, 275.88, 328.4, 389, 388, 422.38,
416.25, 425.2, 466.5, 447.75, 458.5, 450.13, 449.75, 464, 439.25,
424.75, 427.1, 432.88, 433.38, 456.1, 468.25, 487.9, 511.38,
572.75, 629.75, 656.3, 633.88, 651.75, 653.4, 631.25, 635.1,
647.25, 657.25, 628.1, 669, 700.25, 722.1, 709.38, 661.5, 577.9,
587, 644.3, 647.5, 625, 603.9, 611.13, 624.25, 645.63, 612, 591,
584.2, 581.63, 584.75, 597.9, 620.75, 600.38, 589.5, 600, 591.7,
598, 592.25, 591.75, 598.5, 601, 584.13, 574.1, 558.13, 492.7,
424.25, 340, 277.4, 326.88, 312.38, 315.63, 345.82, 338.5, 296.1,
235, 224.88, 221.1, 205.63, 152.75), ContainerTCRIndex = c(101L,
103L, 102L, 104L, 105L, 103L, 106L, 106L, 110L, 109L, 110L, 111L,
114L, 107L, 109L, 109L, 110L, 110L, 110L, 110L, 110L, 109L, 108L,
104L, 103L, 101L, 100L, 97L, 98L, 96L, 91L, 89L, 89L, 89L, 86L,
85L, 84L, 83L, 84L, 83L, 81L, 80L, 80L, 78L, 75L, 72L, 69L, 66L,
65L, 65L, 63L, 58L, 58L, 59L, 61L, 61L, 64L, 67L, 72L, 74L, 74L,
71L, 68L, 71L, 78L, 81L, 86L, 90L, 91L, 91L, 90L, 90L, 90L, 85L,
83L, 81L, 82L, 83L, 82L, 79L, 76L, 72L, 66L, 59L, 55L, 49L, 47L,
48L, 49L, 51L, 56L, 57L, 57L, 59L, 64L, 62L, 63L, 62L, 59L, 62L,
71L, 75L, 79L, 85L, 88L, 93L, 95L, 97L, 95L, 95L, 94L, 104L,
113L, 120L, 124L, 126L, 128L, 131L, 136L, 142L, 146L, 154L, 163L,
169L, 171L, 172L, 172L, 170L, 164L, 159L, 150L, 142L, 124L, 114L,
116L, 111L, 107L, 107L, 111L, 113L, 112L, 112L, 108L, 105L, 99L,
94L, 91L, 96L, 100L, 102L, 103L, 105L, 107L, 109L, 112L, 116L,
116L, 114L, 114L, 113L, 113L, 113L, 111L, 108L, 101L, 97L, 92L,
87L, 65L, 58L, 47L, 45L, 40L, 37L, 35L, 35L, 34L, 34L, 34L, 33L,
33L, 32L, 32L, 32L, 34L, 36L, 41L, 47L, 58L, 61L, 64L, 64L, 61L,
57L, 59L, 66L, 71L, 76L, 75L, 75L, 73L, 65L, 61L, 55L, 48L, 46L,
42L, 40L, 41L, 42L, 43L, 45L, 45L, 44L, 44L, 44L, 43L, 42L, 42L,
42L, 43L, 43L, 45L, 46L, 47L, 47L, 48L, 47L, 48L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 48L, 50L,
52L, 59L, 63L, 62L, 60L, 55L, 51L, 46L, 45L, 43L), USAInterestRates = c(0.08,
0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09,
0.09, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.08, 0.08,
0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05,
0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07,
0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.07, 0.07, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03,
0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03
)), .Names = c("MGOFujairah", "MGORotterdam", "X380CSTFujairah",
"X380CSTRotterdam", "X380CSTHouston", "ContainerTCRIndex", "USAInterestRates"
), class = "data.frame", row.names = c(NA, -255L))
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