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pu.R
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library("forecast")
library("TSA")
library("xts")
library("wmtsa")
od<-read.csv("purchase_by_type.csv")
of<-read.csv("totalfs.csv")
#fitbeg<-1;#20140401
fitbeg<-275;#20140401
fitend<-427;#20140831
prebeg<-fitend+1;
preend<-457;#20140930
fd<-od[fitbeg:fitend,]
report_date <-ts(fd$report_date,fre=7)
total_purchase_amt <-ts(log(fd$total_purchase_amt ),fre=7)
zfb1 <-ts(log(fd$zfb1),fre=7)
zfb2 <-ts(log(fd$zfb2),fre=7)
zfb3 <-ts(log(fd$zfb3),fre=7)
ibank1 <-ts(log(fd$bank1),fre=7)
ibank2 <-ts(log(fd$bank2),fre=7)
ibank3 <-ts(log(fd$bank3),fre=7)
ibank4 <-ts(log(fd$bank4 ),fre=7)
ibank5 <-ts(log(fd$bank5 ),fre=7)
ibank6 <-ts(log(fd$bank6 ),fre=7)
ibank7 <-ts(log(fd$bank7 ),fre=7)
ibank8 <-ts(log(fd$bank8 ),fre=7)
ibank9 <-ts(log(fd$bank9 ),fre=7)
ibank10 <-ts(log(fd$bank10),fre=7)
ibank11 <-ts(log(fd$bank11),fre=7)
share <-ts(log(fd$share),fre=7)
bank1<-ts(log(fd$bank1+fd$bank2+fd$bank3+fd$bank4),fre=7)
bank2<-ts(log(fd$bank5+fd$bank6),fre=7)
bank3<-ts(log(fd$bank7+fd$bank8+fd$bank9+fd$bank10+fd$bank11),fre=7)
#total_purchase_amt<-wavShrink(total_purchase_amt[fitbeg:fitend],n.level=1)
#zfb1 <-wavShrink(zfb1[fitbeg:fitend] ,n.level=1)
#zfb2 <-wavShrink(zfb2[fitbeg:fitend] ,n.level=1)
#bank1<-wavShrink(bank1[fitbeg:fitend],n.level=1)
#bank2<-wavShrink(bank2[fitbeg:fitend],n.level=1)
cat("... begin to loop ...\n")
par(mfcol=c(3,2))
zfb1<-log(exp(zfb1))
zfb2<-log(exp(zfb2)+exp(zfb3))
bank1<-log(exp(bank1)+exp(bank2))
bank2<-log(exp(bank3)+exp(share))
tslist<-list(total_purchase_amt,zfb1,zfb2,bank1,bank2)
type<-list("total_purchase_amt","zfb1","zfb2","bank1","bank2")
#orignal+features
#orderlist<-list(c(4,0,4),c(0,0,0),c(4,0,4),c(1,0,1),c(1,0,5))
orderlist<-list(c(0,0,0),c(0,0,0),c(3,1,4),c(1,0,1),c(1,0,5))
sorderlist<-list(c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0))
#tslist<-list(total_purchase_amt,zfb1,zfb2,zfb3,bank1,bank2,bank3,share)
#type<-list("total_purchase_amt","zfb1","zfb2","zfb3","bank1","bank2","bank3","share")
#sorderlist<-list(c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0),c(0,0,0))
#orderlist<-list(c(0,0,0),c(0,0,0),c(0,0,0),c(4,0,4),c(1,0,6),c(2,1,1),c(1,0,6),c(1,1,1))
#IOlist<-list(c(0),c(106),c(0),c(106),c(0),c(33),c(0),c(0));
#zfb1<-exp(zfb1)
#zfb2<-exp(zfb2)
#bank1<-exp(bank1)
#bank1<-exp(bank1)
#denoised
#orderlist<-list(c(4,0,1),c(4,0,1),c(5,0,1),c(5,0,1),c(4,0,1))
#sorderlist<-list(c(0,1,2),c(0,1,1),c(0,1,1),c(0,1,1),c(0,1,1))
#orignal
#orderlist<-list(c(4,0,1),c(0,0,0),c(4,0,4),c(1,0,1),c(5,0,1))
#sorderlist<-list(c(0,1,2),c(2,1,1),c(2,1,1),c(3,1,2),c(1,1,2))
result<-rep(0,30)
fittedValue<-rep(0,fitend-fitbeg+1)
result.stlf.arima<-rep(0,30)
result.stlf.ets<-rep(0,30)
result.tbats<-rep(0,30)
result.sam<-rep(0,30)
fittedValue.stlf<-rep(0,fitend-fitbeg+1)
result.tsw<-rep(0,30)
#xregfit<-data.frame(of[fitbeg:fitend,2:(NCOL(of)-21)]);
#xregpre<-data.frame(of[prebeg:preend,2:(NCOL(of)-21)]);
#(1)report_date, (2)w1, (3)w2, (4)w3, (5)w4, (6)w5, (7)w6, (8)mb1, (9)mb2, (10)mb3, (11)mb4, (12)mb5, (13)ma1, (14)ma2, (15)ma3, (16)ma4, (17)ma5, (18)fb1, (19)fb2, (20)fb3, (21)fb4, (22)fb5, (23)fb6, (24)fb7, (25)fi1, (26)fi2, (27)fi3, (28)fa1, (29)fa2, (30)fa3, (31)fa4, (32)fa5, (33)fa6, (34)fa7, (35)tx, (36)sb1, (37)sb2, (38)sb3, (39)sb4, (40)sb5, (41)sb6, (42)sb7, (43)si1, (44)sa1, (45)sa2, (46)sa3, (47)sa4, (48)sa5, (49)sa6, (50)sa7, (51)cb1, (52)cb2, (53)cb3, (54)cb4, (55)cb5, (56)cb6, (57)cb7, (58)ci1, (59)ci2, (60)ci3, (61)ci4, (62)ci5, (63)ci6, (64)ci7, (65)ca1, (66)ca2, (67)ca3, (68)ca4, (69)ca5, (70)ca6, (71)ca7
#1
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,24:28],of[fitbeg:fitend,35],of[fitbeg:fitend,42:44]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,24:28],of[prebeg:preend,35],of[prebeg:preend,42:44]);
#2
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,23:29],of[fitbeg:fitend,35],of[fitbeg:fitend,41:45]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,23:29],of[prebeg:preend,35],of[prebeg:preend,41:45]);
#3
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,22:30],of[fitbeg:fitend,35],of[fitbeg:fitend,40:46]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,22:30],of[prebeg:preend,35],of[prebeg:preend,40:46]);
#4
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,21:31],of[fitbeg:fitend,35],of[fitbeg:fitend,39:47]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,21:31],of[prebeg:preend,35],of[prebeg:preend,39:47]);
#5
xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,20:32],of[fitbeg:fitend,35],of[fitbeg:fitend,38:48]);
xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,20:32],of[prebeg:preend,35],of[prebeg:preend,38:48]);
#6
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,19:33],of[fitbeg:fitend,35],of[fitbeg:fitend,37:49]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,19:33],of[prebeg:preend,35],of[prebeg:preend,37:49]);
#7
#xregfit<-data.frame(of[fitbeg:fitend,2:17],of[fitbeg:fitend,18:34],of[fitbeg:fitend,35],of[fitbeg:fitend,36:50]);
#xregpre<-data.frame(of[prebeg:preend,2:17],of[prebeg:preend,18:34],of[prebeg:preend,35],of[prebeg:preend,36:50]);
#xregfit<-of[fitbeg:fitend,2]
#xregpre<-of[prebeg:preend,2]
#for(xi in 3:NCOL(of))
#{
#if(sum(of[fitbeg:fitend,xi])!=0)
#{
#xregfit<-cbind(xregfit,of[fitbeg:fitend,xi])
#xregpre<-cbind(xregpre,of[prebeg:preend,xi])
#}
#}
#for(ti in 2:8)
for(ti in 2:5)
{
#st<-seq(0,0.4,0.05);
st<-0.05
ts<-tslist[[ti]]
#iov<-IOlist[[ti]]
or<-orderlist[[ti]]
sor<-sorderlist[[ti]]
legend<-type[[ti]]
aicb<-Inf;
#vm<-list(model="sGARCH",garchOrder=c(7,6),submodel = NULL, external.regressors = NULL, variance.targeting = FALSE)
#mm<-list(armaOrder=c(5,1),include.mean=FALSE,external.regressors=as.matrix(xregfit))
#mm<-list(armaOrder=c(5,1))
#spec<-ugarchspec(variance.mode=vm,mean.mode=mm,distribution.mode="norm",start.pars = list(), fixed.pars = list())
#ts.igar=ugarchfit(data=ts,spec=spec,solver.control=list(trace=0))
#next
for(si in st)
{
if(si==0) next;
#lm+arima nosize
#tsam<-auto.arima(ts,xreg=xregfit)
#tsam<-arima(ts,order=or,seasonal=list(order=sor,period=7),xreg=xregfit)
#tsam<-arima(ts,order=or,seasonal=list(order=sor,period=7))
#if(ti==3)
#{
#tsam<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(108))
#}else
if(ti==4)
{
tsam<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(33))
}else if(ti==5)
{
tsam<-arima(ts,order=or,xreg=xregfit,io=c(137))
}else
{
tsam<-arima(ts,order=or,xreg=xregfit)
}
#if(ti==6)
#{
#tsam<-arima(ts,order=or,xreg=xregfit,io=c(33))
#}else if(ti==7)
#{
#tsam<-arima(ts,order=or,xreg=xregfit,io=c(137))
#}else if(ti==8)
#{
#tsam<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(86))
#}else
#{
#tsam<-arima(ts,order=or,xreg=xregfit)
#}
#tsam<-arima(ts,order=or,xreg=xregfit)
aico<-tsam$aic;
fixedv<-rep(NA,length(tsam$coef));
needFix<-TRUE;
cat(si,"---------------------------->\n");
#print(tsam)
while(needFix)
{
needFix<-FALSE;
#hasNaN<-FALSE;
#for (ci in 1:length(tsam$coef))
#{
##if(is.na(coeftest(tsam)[ci,4]))
#if(is.na(sqrt(diag(tsam$var.coef))[ci]))
#{
#hasNaN<-TRUE;
#break;
#}
#}
#if(!hasNaN)
##if(0)
#{
vi<-1;
for (ci in 1:length(tsam$coef))
{
if(tsam$coef[ci]==0)
{
next
}else
{
if(!is.na(sqrt(diag(tsam$var.coef))[vi]))
{
if( (1-pnorm(abs(tsam$coef[ci])/sqrt(diag(tsam$var.coef))[vi]))*2 > si )
{
fixedv[ci]=0;
needFix<-TRUE;
}
}
vi<-vi+1
}
}
#}
if(needFix)
{
cat("Fixing...\n")
#tsam<-arima(ts,order=or,seasonal=list(order=sor,period=7),xreg=xregfit,fixed=fixedv,transform.pars = FALSE)
#tsam<-arimax(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE,io=iov)
#tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE)
#tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE,io=c(33,71))
#if(ti==3)
#{
#tsam<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(108),fixed=fixedv,transform.pars = FALSE)
#}else
if(ti==4)
{
tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE,io=c(33))
}else if(ti==5)
{
tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE,io=c(137))
}else
{
tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE)
}
#if(ti==6)
#{
#tsam<-arima(ts,order=or,xreg=xregfit,io=c(33),fixed=fixedv,transform.pars = FALSE)
#}else if(ti==7)
#{
#tsam<-arima(ts,order=or,xreg=xregfit,io=c(137),fixed=fixedv,transform.pars = FALSE)
#}else if(ti==8)
#{
#tsam<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(86),fixed=fixedv,transform.pars = FALSE)
#}else
#{
#tsam<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE)
#}
#tsam<-arima(ts,order=or,seasonal=list(order=sor,period=7),fixed=fixedv,transform.pars = FALSE)
#print(tsam)
}
}
##print(tsam)
if(tsam$aic<aicb)
{
ki<-si;
fixb<-fixedv;
aicb<-tsam$aic;
}
}
#tsam.bestfit<-tsam;
#tsam.bestfit<-arima(ts,order=or,seasonal=list(order=sor,period=7),xreg=xregfit,fixed=fixb,transform.pars = FALSE);
cat("========>",legend,"<======\n")
#if(ti==3)
#{
#tsam.bestfit<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(108),fixed=fixb,transform.pars = FALSE);
#fittedValueTmp<-fitted(tsam.bestfit)
#print(tsam.bestfit);
#tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
#}else
if(ti==4)
{
tsam.bestfit<-arima(ts,order=or,xreg=xregfit,fixed=fixb,transform.pars = FALSE,io=c(33));
fittedValueTmp<-fitted(tsam.bestfit)
print(tsam.bestfit);
tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
}else if(ti==5)
{
tsam.bestfit<-arima(ts,order=or,xreg=xregfit,fixed=fixb,transform.pars = FALSE,io=c(137));
fittedValueTmp<-fitted(tsam.bestfit)
print(tsam.bestfit);
tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
}else
{
tsam.bestfit<-arima(ts,order=or,xreg=xregfit,fixed=fixb,transform.pars = FALSE);
fittedValueTmp<-fitted(tsam.bestfit)
print(tsam.bestfit);
}
#if(ti==6)
#{
#tsam.bestfit<-arima(ts,order=or,xreg=xregfit,io=c(33),fixed=fixb,transform.pars = FALSE)
#fittedValueTmp<-fitted(tsam.bestfit)
#print(tsam.bestfit);
#tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
#}else if(ti==7)
#{
#tsam.bestfit<-arima(ts,order=or,xreg=xregfit,io=c(137),fixed=fixb,transform.pars = FALSE)
#fittedValueTmp<-fitted(tsam.bestfit)
#print(tsam.bestfit);
#tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
#}else if(ti==8)
#{
#tsam.bestfit<-arima(ts,order=or,xreg=data.frame(xregfit,AO=1*(seq(ts)==86)),io=c(85),fixed=fixb,transform.pars = FALSE)
#tsam.bestfit<-arima(ts,order=or,xreg=data.frame(xregfit),io=c(86),fixed=fixb,transform.pars = FALSE)
#fittedValueTmp<-fitted(tsam.bestfit)
#print(tsam.bestfit);
#tsam.bestfit$coef<-tsam.bestfit$coef[1:(length(tsam.bestfit$coef)-1)]
#}else
#{
#tsam.bestfit<-arima(ts,order=or,xreg=xregfit,fixed=fixedv,transform.pars = FALSE)
#fittedValueTmp<-fitted(tsam.bestfit)
#print(tsam.bestfit);
#}
#tsam.bestfit<-arima(ts,order=or,xreg=xregfit,fixed=fixb,transform.pars = FALSE);
#tsam.bestfit<-arima(ts,order=or,seasonal=list(order=sor,period=7),xreg=xregfit);
#tsam.bestfit<-arima(ts,order=or,seasonal=list(order=sor,period=7),fixed=fixb,transform.pars = FALSE);
#tsam.bestfit<-arima(ts,order=or,seasonal=list(order=sor,period=7));
detectAO(tsam.bestfit);
detectIO(tsam.bestfit);
cat("si=",ki,"aicb= ",aico,"->",aicb,"\n")
#if(ti==4)
#{
#tsam.p<-predict(tsam.bestfit,newxreg =data.frame(xregpre,rep(0,30)));
#}else
#{
#tsam.p<-predict(tsam.bestfit,newxreg =xregpre);
#}
#if(ti==8)
#{
#tsam.p<-predict(tsam.bestfit,newxreg =data.frame(xregpre,rep(0,30)));
#}else
#{
tsam.p<-predict(tsam.bestfit,newxreg =xregpre);
#}
#tsam.p<-predict(tsam.bestfit,n.ahead=30);
tsamp<-tsam.p$pred;
print(exp(tsamp))
result<-result+exp(tsamp)
fittedValue<-fittedValue+exp(fittedValueTmp)
tsam.data<-ts(c(exp(ts),od[prebeg:preend,2]),fre=7)
tsam.fore<-ts(exp(c(fittedValueTmp,tsamp)),fre=7)
tsam.data.xts<-xts(tsam.data,seq(as.POSIXct("2014-04-01"),len=length(tsam.data),by='day'))
tsam.fore.xts<-xts(tsam.fore,seq(as.POSIXct("2014-04-01"),len=length(tsam.fore),by='day'))
plot(as.zoo(cbind(tsam.data.xts,tsam.fore.xts)),col=1:2,lty=1:2,screens=1,xlab="Time")
legend(x="topright",legend=c("Observed",legend),lty=1:2,col=1:2)
#acf(residuals(tsam.bestfit))
#pacf(residuals(tsam.bestfit))
#acf(diff(residuals(tsam.bestfit)))
#pacf(diff(residuals(tsam.bestfit)))
#acf(diff(residuals(tsam.bestfit),lag=7))
#pacf(diff(residuals(tsam.bestfit),lag=7))
#acf(abs(residuals(tsam.bestfit)))
#pacf(abs(residuals(tsam.bestfit)))
#acf(diff(abs(residuals(tsam.bestfit))))
#pacf(diff(abs(residuals(tsam.bestfit))))
#acf(residuals(tsam.bestfit)^2)
#pacf(residuals(tsam.bestfit)^2)
#######
#TODO:
#obtain month/holiday features from arima.
coefs <- tsam.bestfit$coef
narma<-sum(tsam.bestfit$arma[1:4])
if(names(coefs)[narma + 1L] == "intercept") narma<-narma+1
shift<-coefs[-(1:narma)]
holidayshift<-rep(1,30)
for(fi in 7:NCOL(shift))
{
holidayshift<-holidayshift*exp(xregfit[,fi]*shift[fi])
}
monthshift<-rep(1,30)
for(fi in 17:NCOL(shift))
{
monthshift<-monthshift*exp(xregfit[,fi]*shift[fi])
}
###stlf.arima###
tsst.arima<-stlf(ts,h=30,s.window=7,method='arima',ic='bic',xreg=xregfit,newxreg=xregpre)
result.stlf.arima<-result.stlf.arima+exp(tsst.arima$mean)
fittedValue.stlf<-fittedValue.stlf+exp(fitted(tsst.arima))
###stlf.ets###
tsst.ets<-stlf(ts,h=30,s.window=7,method='ets',ic='bic',opt.crit='mae')
result.stlf.ets<-result.stlf.ets+exp(tsst.ets$mean)*holidayshift*monthshift
###tbats###
tsm1<-msts(ts,seasonal.periods=c(7,30.44))
tbats<-tbats(tsm1)
tbatsp<-forecast(tbats,30)
result.tbats<-result.tbats+exp(tbatsp)*holidayshift
#plot(tstbats1)
#TODO:
#use wavelet ,arima for each wave.
tsw3<-wavMODWT(ts,n.level=3)
tswp<-rep(0,30)
for(tswi in 1:4)
{
tsw<-auto.arima(tsw3$data[tswi],xreg=xregfit)
TODO: fix
tsw.p<-predict(tsw,newxreg=xregpre)
tswp<-tswp+tws.p$pred
}
result.tsw<-result.tsw+exp(tswp)
#seasonal arima + holiday shift
tssam<-arima(ts,order=c(0,0,0),seasonal=list(order=c(0,1,0)r,period=7))
tssam.p<-predict(tssam,30)
tssamp<-tssam.p$pred
result.sam<-result.sam+exp(tssam)*holidayshift*monthshift
TODO: fix
}
print(result)
total.data<-ts(c(od[fitbeg:preend,2]),fre=7)
total.fore<-ts(c(fittedValue,result),fre=7)
total.data.xts<-xts(total.data,seq(as.POSIXct("2014-04-01"),len=length(total.data),by='day'))
total.fore.xts<-xts(total.fore,seq(as.POSIXct("2014-04-01"),len=length(total.fore),by='day'))
plot(as.zoo(cbind(total.data.xts,total.fore.xts)),col=1:2,lty=1:2,screens=1,xlab="Time")
legend(x="topright",legend=c("Observed","total"),lty=1:2,col=1:2)
#print(result.stlf.arima)
#total.data.stlf<-ts(c(exp(total_purchase_amt[fitbeg:fitend]),exp(total_purchase_amt[prebeg:preend])),fre=7)
#total.fore.stlf<-ts(c(fittedValue.stlf,result.stlf.arima),fre=7)
#total.data.stlf.xts<-xts(total.data.stlf,seq(as.POSIXct("2014-04-01"),len=length(total.data.stlf),by='day'))
#total.fore.stlf.xts<-xts(total.fore.stlf,seq(as.POSIXct("2014-04-01"),len=length(total.fore.stlf),by='day'))
#plot(as.zoo(cbind(total.data.stlf.xts,total.fore.stlf.xts)),col=1:2,lty=1:2,screens=1,xlab="Time")
#legend(x="topright",legend=c("Observed","total"),lty=1:2,col=1:2)
fixPar<-function(am)
{
fix<-am$call$fixed
fixedv<-if(!is.null(fix)) eval.parent(fix) else rep(NA,length(am$coef));
needFix<-FALSE;
vi<-1;
for (ci in 1:length(am$coef))
{
if(am$coef[ci]==0)
{
next
}else
{
if(!is.na(sqrt(diag(am$var.coef))[vi]))
{
if( (1-pnorm(abs(am$coef[ci])/sqrt(diag(am$var.coef))[vi]))*2 > si )
{
fixedv[ci]=0;
needFix<-TRUE;
}
}
vi<-vi+1
}
}
}