I am doing credit risk modelling on costumer transaction data a part of which looks like this : str(x)'data.frame':   412516 obs. of  26 variables: $ Tenure           : num  1.26 1.25 1.26 1.31 1.32 ... $ Product          : Factor w/ 24 levels BACKHOE LOADER,..: 4 4 4 9 9 9 9 9 9 9 ... $ Net.Exposure     : num  333339 528049 327335 350000 460000 ... $ OD.On.31.01.2017 : num  0 90386 0 0 1099692 ... $ LM.Bucket        : Ord.factor w/ 11 levels 0<1 TO  30<..: 1 1 1 1 11 11 11 11 11 11 ... $ Bucket           : Ord.factor w/ 11 levels 0<1 TO  30<..: 1 3 1 1 11 11 11 11 11 11 ... $ Billing          : num  65380 0 8800 6339 8331 ... $ Fin.IRR          : num  13.5 14.6 14.6 18.1 23.3 ... $ NPA.Flag         : Factor w/ 2 levels No,Yes: 1 1 1 1 2 2 2 2 2 2 ... $ Inst.Due         : num  0 0.85 0 0 3 3 3 3 3 3 ... $ FR.On.31.01.2017 : num  65380 0 38940 35043 499860 ... $ POS.On.31.01.2017: num  56453 0 32920 33368 293943 ... $ Del.String       : int  2 1 1 1 53720 53720 53720 53720 53720 53720 ... $ Territory        : Factor w/ 43 levels AGRA,AHMEDABAD,..: 41 41 41 41 41 41 41 41 41 41 ...The variables like OD(Overdue) and LM.Bucket( How many months he has been due on his loan payment till last month) change every month .I have 2 tasks :Predict Bucket and NPA Flag(Non performing asset)I built a model for this based only on the Jan data(x). But my question is since these variables change every month, should i treat this as a sequential data and build a deep learning model(HMM/NN) on it? If i should what should I do with the static variables like Product type etc.?I asked my boss regarding the same and he said it shouldn't be done because external economy factor change with time. Is that a reason for concern?
Supervised Learning on Sequential data
neural network;classification;deep learning;sequential pattern mining