[SOLVED] Arima loop vs Arima function [R]

Issue

I am trying to build a function where I estimate a lot of arima models with a for loop.

I got the for loops running and getting me my desired output, but as soon as I try to get my code into the function I get errors.

This is the loop:

model_acc <- data.frame()
for (p in 0:4) {
  for(d in 0:2){
    for (q in 0:4){
      
      model <- arima(data, order=c(p,d,q), method = "ML")
      acc <- accuracy(model)
      acc <- as.data.frame(acc)
      acc_ext <- data.frame(acc,
                            loglikeli=logLik(model),
                            AIC=AIC(model),
                            BIC=BIC(model),
                            order=paste(p,d,q,sep=","))
      
      acc_ext <- select(acc_ext,
                        ME, RMSE, MAE, MAPE,loglikeli,AIC,BIC,order)
      
      model_acc <- rbind(model_acc, acc_ext)
    }
  }
}

I am aware that there are some models that cannot be computed with Maximum Likelihood, due to the constraints in the optimization. But this loop gets me 61 models out of 75 (just with method="CSS"). I get the models that could be computed.

So the parameters I’d like to move are: data, p_max, d_max, q_max, and, method.

So the function goes like this:

which_arima <- function(data, p_max, d_max, q_max, method){

 model_acc <- data.frame()
  for (p in 0:p_max) {
    for(d in 0:d_max){
      for (q in 0:q_max){
        
        model <- arima(data, order=c(p,d,q), method = method)
        acc <- accuracy(model)
        acc <- as.data.frame(acc)
        acc_ext <- data.frame(acc,
                              loglikeli=logLik(model),
                              AIC=AIC(model),
                              BIC=BIC(model),
                              order=paste(p,d,q,sep=","))
        
        acc_ext <- select(acc_ext,
                          ME, RMSE, MAE, MAPE,loglikeli,AIC,BIC,order)
        
        model_acc <- rbind(model_acc, acc_ext)
      }
    }
  }
  return(model_acc)
}

a <- which_arima(data, p_max=4, d_max=2, q_max=4, method="ML")

But when I execute it, I get this error (referring to the models that could not be computed) and don’t get anything. (in the for loop only I got the models that could be computed).

Error in optim(init[mask], armafn, method = optim.method, hessian = TRUE,  : 
  non-finite finite-difference value [4]
In addition: Warning messages:
1: In arima(data, order = c(p, d, q), method = method) :
  possible convergence problem: optim gave code = 1
2: In arima(data, order = c(p, d, q), method = method) :
  possible convergence problem: optim gave code = 1
3: In log(s2) : NaNs produced
4: In log(s2) : NaNs produced
Called from: optim(init[mask], armafn, method = optim.method, hessian = TRUE, 
    control = optim.control, trans = as.logical(transform.pars))
Browse[1]> Q

What is going wrong? Because without the function environment is working "fine". And more importantly, how can I solve this?

Thanks in advance!!


Here is the data:

> dput(data)
structure(c(1.04885832686158, 1.06016074629379, 1.0517956106758, 
1.02907998600003, 1.05054370620123, 1.07261670636915, 1.0706491823234, 
1.0851355199628, 1.08488055975672, 1.08085233559646, 1.081489249884, 
1.08587205516048, 1.07249155362154, 1.05497731364761, 1.05675866316574, 
1.06428371643968, 1.06065865122313, 1.05621234529568, 1.05339905298902, 
1.05787030302435, 1.0658034000068, 1.08707776713932, 1.08626056161822, 
1.10238697375394, 1.11390088086972, 1.12120513732074, 1.11937921359653, 
1.10341241626668, 1.1156190247407, 1.12376155972358, 1.12411603174635, 
1.12183475077377, 1.12994175229071, 1.12956170931204, 1.12199732095331, 
1.11645064755987, 1.12481242467782, 1.13066151473637, 1.13028712061827, 
1.12694056065497, 1.12382226475179, 1.12352013167586, 1.13391069257413, 
1.14763982976838, 1.14481816405703, 1.14852949174863, 1.14182560351963, 
1.14086563926171, 1.14491904045717, 1.14897189333479, 1.14616964486707, 
1.15074750127031, 1.14681353487065, 1.11151754535415, 1.10497749493861, 
1.10963378437214, 1.12415745716768, 1.17507535290893, 1.20285968503846, 
1.22784769136553, 1.23940795216891, 1.254741010879, 1.29442450660416, 
1.30428779451896, 1.31314618462517, 1.32544236970695, 1.33728107423435, 
1.34408499591568, 1.34199331033196, 1.34027541040719, 1.33616830504407, 
1.33360421057602, 1.33332422301893, 1.34717794252774, 1.3502492092262, 
1.35168291803248, 1.35827816606688, 1.36772644852242, 1.36755741578293, 
1.36926148542701, 1.37264481021763, 1.37322962601678, 1.37643913938007, 
1.37906284181634, 1.37644362054554, 1.38911039237937, 1.39412557349575, 
1.40094895608589, 1.40630864159528, 1.40823485306921, 1.4138446752069, 
1.42340582796496, 1.43641264727375, 1.43605231080207, 1.44839810240334, 
1.45451041581127, 1.46166006472498, 1.46774816064695, 1.46930608347752, 
1.47885183796249, 1.49059366171423, 1.49849145403671, 1.51209667142067, 
1.5250141727637, 1.5392257264567, 1.55144303632514, 1.56488453313021, 
1.58308777691125, 1.59737589266492, 1.60896279958586, 1.62553339664661, 
1.63594174408691, 1.65233080464302, 1.67114336171075, 1.6897476078746, 
1.71673790971729, 1.74453973794979, 1.76317526009814, 1.79187692264759, 
1.84186982937622, 1.9460629324144, 2.05986108970089, 2.06767436493269, 
2.0783176148561, 2.08271855277262, 2.09358626977224, 2.09674958523685, 
2.11582742548029, 2.12810020369675, 2.13596929171732, 2.13972610568317, 
2.14456803530813, 2.15013985201827, 2.16007349878874, 2.17165498940627, 
2.18057666565755, 2.19162746118342, 2.20308765886345, 2.21304799942168, 
2.22367586966847, 2.23629862083737, 2.24751866055731, 2.26100586740225, 
2.40972893063106, 2.60366275683037, 2.68572993101095, 2.70501080420283, 
2.6676315643757, 2.6479269687206, 2.64641010174172, 2.69966594490103, 
2.69665303568271, 2.71396750774502, 2.71900427132191, 2.72876269360869, 
2.76276620421252, 2.76620189252239, 2.74632816231219, 2.74196673817286, 
2.72905831066292, 2.75190757584346, 2.77801573354251, 2.84089580821293, 
2.85681823660541, 2.84754572013613, 2.85858396073969, 2.86184353545653, 
2.86958309986952, 2.94279115543111, 2.98631808884879, 3.00648449252989, 
3.00620698598987, 3.15207693676406, 3.27614511764022, 3.32011714920345, 
3.39367422894347, 3.64822360464499, 3.61835354049394, 3.59374251055335, 
3.63237359915986, 3.62209957896007, 3.64554153297999, 3.71611226971083, 
3.76031231050606, 3.80307769833913, 3.77959145461296, 3.74772344909971, 
3.95072671083008, 4.03652777624058, 4.06630193640976, 4.08838169421096, 
4.09074775372752, 4.09286687677964, 4.11466378890098, 4.14350067096966, 
4.18153835521181, 4.21299240125327, 4.23975062689892, 4.26683207875595, 
4.29265054707555, 4.31835343358436, 4.34946580314932, 4.37865522989399, 
4.41294135451665), .Dim = c(204L, 1L), .Dimnames = list(NULL, 
    "price"), .Tsp = c(2004, 2020.91666666667, 12), class = "ts")

Solution

We could add a tryCatch in the function

which_arima <- function(data, p_max, d_max, q_max, method){

 model_acc <- data.frame()
  for (p in 0:p_max) {
    for(d in 0:d_max){
      for (q in 0:q_max){
        
        tryCatch({
           model <- arima(data, order=c(p,d,q), method = method)
        acc <- accuracy(model)
        acc <- as.data.frame(acc)
        acc_ext <- data.frame(acc,
                              loglikeli=logLik(model),
                              AIC=AIC(model),
                              BIC=BIC(model),
                              order=paste(p,d,q,sep=","))
        
        acc_ext <- select(acc_ext,
                          ME, RMSE, MAE, MAPE,loglikeli,AIC,BIC,order)
        
        model_acc <- rbind(model_acc, acc_ext)
        }, error = function(e) NA)
        
        
        
      }
    }
  }
  
  
  
  return(model_acc)

}

-testing

a <- which_arima(data, p_max=4, d_max=2, q_max=4, method="ML")

-output

> a
                          ME       RMSE        MAE       MAPE  loglikeli        AIC        BIC order
Training set    3.916595e-14 1.00150757 0.84665890 47.3734354 -289.77077  583.54155  590.17779 0,0,0
Training set1   1.507413e-03 0.50685119 0.42608540 23.8330904 -153.49920  312.99840  322.95276 0,0,1
Training set2   1.477754e-03 0.27462038 0.23150111 12.9162286  -31.20907   70.41814   83.69062 0,0,2
Training set3   1.349691e-03 0.16826013 0.13265807  7.3234273   67.17326 -124.34652 -107.75592 0,0,3
Training set4   1.205197e-03 0.12347033 0.09404764  5.1708085  132.56865 -253.13729 -233.22857 0,0,4
Training set5   1.649574e-02 0.03945226 0.02063318  0.9365795  367.68785 -733.37570 -730.06250 0,1,0
Training set6   1.103986e-02 0.03456075 0.01736215  0.7957414  394.41586 -784.83172 -778.20531 0,1,1
Training set7   1.033720e-02 0.03443721 0.01713550  0.7848747  395.13798 -784.27595 -774.33634 0,1,2
Training set8   9.546954e-03 0.03417545 0.01651661  0.7683963  396.69035 -785.38071 -772.12788 0,1,3
Training set9   8.268413e-03 0.03353547 0.01710311  0.7855244  400.43015 -790.86030 -774.29427 0,1,4
Training set10  1.081905e-04 0.03982073 0.01849307  0.8429273  363.50025 -725.00049 -721.69223 0,2,0
Training set11  2.800510e-03 0.03429965 0.01750163  0.8103622  392.52320 -781.04639 -774.42986 0,2,1
Training set12  2.920421e-03 0.03214346 0.01515181  0.7129633  405.66898 -805.33795 -795.41315 0,2,2
Training set13  2.915234e-03 0.03206868 0.01541923  0.7234715  406.11610 -804.23221 -790.99914 0,2,3
Training set14  2.915216e-03 0.03206786 0.01543761  0.7239875  406.11852 -802.23704 -785.69571 0,2,4
Training set15  1.609540e-02 0.03954680 0.02075934  0.9489873  365.76961 -725.53923 -715.58487 1,0,0
Training set16  1.067822e-02 0.03464237 0.01747532  0.8057485  392.50610 -777.01221 -763.73973 1,0,1
Training set17  7.714409e-03 0.03500020 0.01712196  0.8100354  390.85979 -771.71958 -755.12898 1,0,2
Training set18  9.510129e-03 0.03417676 0.01653561  0.7702435  398.64834 -785.29668 -765.38796 1,0,3
Training set19  9.299540e-03 0.03407723 0.01644942  0.7661016  399.22596 -784.45192 -761.22508 1,0,4
Training set20  8.521452e-03 0.03440107 0.01658612  0.7665062  395.36364 -786.72729 -780.10088 1,1,0
Training set21  9.502976e-03 0.03434348 0.01673934  0.7705014  395.69269 -785.38538 -775.44577 1,1,1
Training set22  3.638516e-03 0.03220174 0.01508764  0.7126483  408.13770 -808.27541 -795.02259 1,1,2
Training set23  3.626362e-03 0.03212825 0.01534293  0.7227711  408.58054 -807.16108 -790.59505 1,1,3
Training set24  8.353323e-03 0.03319389 0.01722817  0.8063780  402.38983 -792.77965 -772.90042 1,1,4
Training set25  1.322429e-04 0.03862934 0.01853910  0.8452931  369.60607 -735.21213 -728.59560 1,2,0
Training set26  2.950783e-03 0.03271462 0.01554742  0.7271035  402.15143 -798.30287 -788.37806 1,2,1
Training set27  2.918645e-03 0.03207500 0.01535616  0.7214170  406.08191 -804.16382 -790.93075 1,2,2
Training set28  2.915432e-03 0.03206844 0.01542446  0.7236258  406.11678 -802.23356 -785.69222 1,2,3
Training set29  2.892408e-03 0.03184546 0.01585528  0.7398747  407.44682 -802.89365 -783.04404 1,2,4
Training set30  3.275778e-02 0.06802502 0.03811120  1.7010099  257.85907 -507.71814 -494.44566 2,0,0
Training set31  9.458801e-03 0.03434793 0.01677640  0.7737224  397.64430 -785.28860 -768.69800 2,0,1
Training set32  1.041047e-02 0.03449857 0.01757479  0.8092751  393.34656 -774.69312 -754.78440 2,0,2
Training set33  1.036041e-02 0.03438249 0.01712067  0.7851881  397.17474 -780.34949 -757.12265 2,0,3
Training set34  9.291907e-03 0.03413569 0.01668650  0.7780739  395.47305 -774.94611 -748.40115 2,0,4
Training set35  8.657322e-03 0.03439622 0.01656361  0.7657220  395.39192 -784.78384 -774.84422 2,1,0
Training set36  8.975188e-03 0.03415064 0.01646538  0.7625588  396.82841 -785.65683 -772.40401 2,1,1
Training set37  3.623756e-03 0.03213180 0.01528195  0.7207391  408.54688 -807.09376 -790.52773 2,1,2
Training set38  3.632392e-03 0.03218922 0.01509295  0.7124041  408.20813 -804.41627 -784.53703 2,1,3
Training set39  3.593594e-03 0.03190425 0.01582339  0.7407521  409.90942 -805.81883 -782.62639 2,1,4
Training set40  2.046999e-04 0.03534316 0.01743186  0.8004223  387.39069 -768.78138 -758.85658 2,2,0
Training set41  2.900379e-03 0.03229554 0.01543942  0.7252999  404.68622 -801.37243 -788.13936 2,2,1
Training set42  2.912130e-03 0.03206051 0.01549744  0.7258632  406.16110 -802.32220 -785.78086 2,2,2
Training set43  2.748199e-03 0.03106662 0.01710118  0.8027724  411.95382 -811.90764 -792.05804 2,2,3
Training set44  2.757572e-03 0.03048849 0.01571731  0.7454319  413.92678 -813.85355 -790.69568 2,2,4
Training set45  8.190706e-03 0.03447649 0.01665674  0.7750253  393.49946 -776.99891 -760.40831 3,0,0
Training set46  8.485733e-03 0.03422971 0.01656490  0.7726290  394.93100 -777.86199 -757.95327 3,0,1
Training set47  9.212683e-03 0.03436951 0.01678990  0.7781612  393.54762 -773.09523 -749.86839 3,0,2
Training set48  8.721991e-03 0.03406162 0.01638032  0.7597073  399.31535 -782.63070 -756.08574 3,0,3
Training set49 -1.095108e-03 0.03200273 0.01626031  0.8020173  407.59681 -797.19361 -767.33053 3,0,4
Training set50  6.642458e-03 0.03334238 0.01646485  0.7579776  401.61737 -795.23474 -781.98192 3,1,0
Training set51  3.614071e-03 0.03235398 0.01536258  0.7247132  407.14878 -804.29756 -787.73153 3,1,1
Training set52  3.626052e-03 0.03212051 0.01541776  0.7251026  408.62481 -805.24962 -785.37038 3,1,2
Training set53  3.434470e-03 0.03112232 0.01708791  0.8047847  414.43876 -814.87751 -791.68507 3,1,3
Training set54  3.429177e-03 0.03037721 0.01633882  0.7892697  417.28525 -818.57050 -792.06486 3,1,4
Training set55  2.343659e-04 0.03506668 0.01740936  0.7937255  388.95388 -769.90777 -756.67470 3,2,0
Training set56  2.921378e-03 0.03207232 0.01556489  0.7249596  406.11547 -802.23095 -785.68961 3,2,1
Training set57  2.923439e-03 0.03200307 0.01554917  0.7264361  406.53973 -801.07945 -781.22984 3,2,2
Training set58  2.772438e-03 0.03033715 0.01644824  0.7949022  414.69079 -815.38158 -792.22370 3,2,3
Training set59  2.758142e-03 0.03032083 0.01638087  0.7893286  414.81461 -813.62923 -787.16309 3,2,4
Training set60  6.105981e-03 0.03341497 0.01657335  0.7683822  399.75129 -787.50258 -767.59386 4,0,0
Training set61  7.918597e-03 0.03223310 0.01733992  0.8410424  404.54182 -793.08364 -766.53868 4,0,2
Training set62  3.580192e-03 0.03210903 0.01545304  0.7266964  410.69767 -803.39533 -773.53225 4,0,3
Training set63  9.682367e-03 0.03234607 0.01684835  0.8031973  407.24757 -794.49514 -761.31394 4,0,4
Training set64  6.558516e-03 0.03333914 0.01646740  0.7571758  401.63677 -793.27354 -776.70751 4,1,0
Training set65  6.614327e-03 0.03334172 0.01646714  0.7576681  401.62115 -791.24231 -771.36307 4,1,1
Training set66  3.601945e-03 0.03225054 0.01523192  0.7204685  407.38418 -800.76837 -777.57593 4,1,2
Training set67  3.435674e-03 0.03038226 0.01636894  0.7939351  417.16875 -818.33749 -791.83184 4,1,3
Training set68  3.441183e-03 0.03057401 0.01590164  0.7605592  416.07385 -814.14770 -784.32885 4,1,4
Training set69  2.783446e-04 0.03453279 0.01699742  0.7813553  391.99285 -773.98571 -757.44437 4,2,0
Training set70  2.922130e-03 0.03191875 0.01548585  0.7279757  407.03673 -802.07347 -782.22386 4,2,1
Training set71  2.921712e-03 0.03191246 0.01550785  0.7286895  407.07153 -800.14306 -776.98519 4,2,2
Training set72  2.757144e-03 0.03032018 0.01638662  0.7906756  414.79253 -813.58506 -787.11892 4,2,3
Training set73  2.776647e-03 0.03052505 0.01588780  0.7626634  413.36293 -808.72586 -778.95146 4,2,4

Answered By – akrun

Answer Checked By – Senaida (BugsFixing Volunteer)

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