ทำวิจัย สร้างแบบจำลอง ARIMA พยากรณ์ ทีนี้ก็สร้างแบบจำลองหลายๆ แบบ
แล้วคืออาจารย์ ถามว่าทำไมถึงเลือกพิจารณาที่ค่า AIC มากกว่าค่า BIC (SC) เพื่อเลือกแบบจำลองที่เหมาะสมที่สุด

ลองหาใน wiki แต่แปลแล้วยัง งงๆ ไม่รู้ว่าจะเขียนอธิบายในงานวิจัยยังไง
[Spoil] คลิกเพื่อดูข้อความที่ซ่อนไว้Comparison with BIC
The AIC penalizes the number of parameters less strongly than does the Bayesian information criterion (BIC). A comparison of AIC/AICc and BIC is given by Burnham & Anderson (2002, §6.4). The authors show that AIC and AICc can be derived in the same Bayesian framework as BIC, just by using a different prior. The authors also argue that AIC/AICc has theoretical advantages over BIC. First, because AIC/AICc is derived from principles of information; BIC is not, despite its name. Second, because the (Bayesian-framework) derivation of BIC has a prior of 1/R (where R is the number of candidate models), which is "not sensible", since the prior should be a decreasing function of k. Additionally, they present a few simulation studies that suggest AICc tends to have practical/performance advantages over BIC. See too Burnham & Anderson (2004).

Further comparison of AIC and BIC, in the context of regression, is given by Yang (2005). In particular, AIC is asymptotically optimal in selecting the model with the least mean squared error, under the assumption that the exact "true" model is not in the candidate set (as is virtually always the case in practice); BIC is not asymptotically optimal under the assumption. Yang additionally shows that the rate at which AIC converges to the optimum is, in a certain sense, the best possible.

For a more detailed comparison of AIC and BIC, see Aho et al. (2014).
Credit: http://en.wikipedia.org/wiki/Akaike_information_criterion#Comparison_with_BIC

อันนี้ค่า Correlogram ที่ 1 diff
[Spoil] คลิกเพื่อดูข้อความที่ซ่อนไว้
       AC                                      PAC                       Q-Stat             Prob                
1    -0.0451607875125    -0.0451607875125    15.0066177621    0.000107134819042
2    -0.0281354750173    -0.0302366392729    20.8320394832    2.99488467022e-05
3    0.020195897022           0.0175732212409    23.8339948434    2.70548386415e-05
4    0.0245625618222    0.0255406304604    28.275031235    1.09700746704e-05
5    -0.0521299848994    -0.0489205993806    48.2815392428    3.11159453759e-09
6    0.0099501835883    0.00641125040256    49.0105221973    7.41988170905e-09
7    -0.0103307110425    -0.0133989843533    49.7964356338    1.58368034242e-08
8    -0.0167569166539    -0.0161595642064    51.8644883849    1.78760936054e-08
9    0.00970910607806    0.0097761630709    52.5588584683    3.54330722541e-08
10    0.0083713580564    0.00590063416731    53.0751362895    7.2105135307e-08
11    0.00375126881516    0.00693614399026    53.1788191901    1.66573354443e-07
12    0.0332436341661    0.0335040176161    61.3226153319    1.29427899509e-08
13    0.0144383247075    0.0156695642193    62.8590095427    1.60978728125e-08
14    -0.00322847951398    0.00050465110265    62.9358383832    3.56239474586e-08
15    0.0135302509355    0.0130625361169    64.2854197566    4.55730007065e-08
16    0.0186937996872    0.0182393196616    66.8619883862    3.51383382391e-08
17    -0.0253422927933    -0.0200461372219    71.5978339785    1.14267945106e-08
18    0.0069102779023    0.00681923555862    71.950006884    2.10817157065e-08
19    0.0316902784391    0.0305302678044    79.3575846677    2.39906905453e-09
20    0.0168852936169     0.022779408044    81.4608814083    2.21491136543e-09
21    -0.00668778038636    -0.000969915246636    81.790875781    4.04442634938e-09
22    -0.0142098491274    -0.0181198128749    83.2808566143    4.63928528927e-09
23    0.0103246853871    0.00773448847624    84.0675665413    6.88359214074e-09
24    0.0132250420061    0.0140245028815    85.3585311425    8.33526603294e-09
25    0.0262753147061    0.0285215111356    90.4550696654    2.41099384901e-09
26    -0.00602232862803    -0.00228223350451    90.7228431761    4.25407220561e-09
27    0.0144853492921    0.0138236421189    92.2722159369    4.60813132097e-09
28    -0.0018558603272    -0.0026791860664    92.2976518784    8.64144067414e-09
29    0.0260213578366    0.0265050336076    97.2988772854    2.63430799397e-09
30    -0.0148195736587    -0.0113654114236    98.9212372607    2.74655487242e-09
31    0.00626948172562    0.00310635904834    99.2116384423    4.59499849281e-09
32    0.0354802952097    0.035601154845    108.513478085    2.97508573333e-10
33    -0.0374359262254    -0.0346468260091    118.870404105    1.25337518142e-11
34    -0.00437853073199    -0.00371899794932    119.012104098    2.3002266758e-11
35    0.0261677546021    0.0195447319305    124.073907447    6.83608725183e-12
36    0.0112769425063    0.0131241308541    125.01409483    9.28435106573e-12


ทดสอบแล้วไม่มีปัญหา Autocorrelation แต่ไม่รู้ว่าจะใช้ AR กับ MA ที่ lag ไหนมาสร้างแบบจำลองดี
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