My lack of understanding of R is causing me to grind to a halt in my work and seek your help. I'm looking to build a neural network from some time series data and then build a prediction using separate data and the model returned by the trained neural network.
I created an xts containing the dependent variable nxtCl (a one-day forward closing stock price) and the independent variables (a set of corresponding prices and technical indicators).
I split the xts in two, one set being training data and the other set for testing/prediction, these are miData.train and miData.test respectively. Subsequently I altered these two xts to be scaled data frames.
miData.train <- scale(as.data.frame(miData.train))
miDate.test <- scale(as.data.frame(miData.test))
Using the package nnet I am able to build a neural network from the training data:
nn <- nnet(nxtCl ~ .,data=miData.train,linout=T,size=10,decay=0.001,maxit=10000)
The str() output for this returned formula object is:
> str(nn)
List of 18
$ n : num [1:3] 11 10 1
$ nunits : int 23
$ nconn : num [1:24] 0 0 0 0 0 0 0 0 0 0 ...
$ conn : num [1:131] 0 1 2 3 4 5 6 7 8 9 ...
$ nsunits : num 22
$ decay : num 0.001
$ entropy : logi FALSE
$ softmax : logi FALSE
$ censored : logi FALSE
$ value : num 4.64
$ wts : num [1:131] 2.73 -1.64 1.1 2.41 1.36 ...
$ convergence : int 0
$ fitted.values: num [1:901, 1] -0.465 -0.501 -0.46 -0.431 -0.485 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:901] "2005-07-15" "2005-07-18" "2005-07-19" "2005-07-20" ...
.. ..$ : NULL
$ residuals : num [1:901, 1] -0.0265 0.0487 0.0326 -0.0384 0.0632 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:901] "2005-07-15" "2005-07-18" "2005-07-19" "2005-07-20" ...
.. ..$ : NULL
$ call : language nnet.formula(formula = nxtCl ~ ., data = miData.train, inout = T, size = 10, decay = 0.001, maxit = 10000)
$ terms : language nxtCl ~ Op + Hi + Lo + Cl + vul + smaten + smafif + smath + vol + rsi + dvi
$ coefnames : chr [1:11] "Op" "Hi" "Lo" "Cl" ...
$ xlevels : Named list()
- attr(*, "class")= chr [1:2] "nnet.formula" "nnet"
I then try to run the prediction function using this model nn and the data I kept separate miData.test using the following function:
preds <- predict(object=nn, miData.test)
and I get the following error:
Error in terms.default(object, data = data) :
no terms component nor attribute
Running terms.default on miData.test I see that my data frame does not have any attributes:
terms.default(miData.test)
Error in terms.default(miData.test) : no terms component nor attribute
but is this why the prediction will not run?
miData.test has names that match the terms of nn:
> nn$terms
nxtCl ~ Op + Hi + Lo + Cl + vul + smaten + smafif + smath + vol +
rsi + dvi
> names(miData.test)[1] "Op" "Hi" "Lo" "Cl" "vul" "smaten" "smafif" "smath" "vol" "rsi" "dvi" "nxtCl"
And, in terms of structure, the data is exactly the same as that which was used to build nn in the first place. I tried adding my own named attributes to miData.test, matching the terms of nn but that did not work. The str() of miData.test returns:
> str(miData.test)
'data.frame': 400 obs. of 12 variables:
$ Op : num 82.2 83.5 80.2 79.8 79.8 ...
$ Hi : num 83.8 84.2 83 79.9 80.2 ...
$ Lo : num 81 82.7 79.2 78.3 78 ...
$ Cl : num 83.7 82.8 79.2 79 78.2 ...
$ vul : num 4.69e+08 2.94e+08 4.79e+08 3.63e+08 3.17e+08 ...
$ smaten: num 84.1 84.1 83.8 83.3 82.8 ...
$ smafif: num 86.9 86.8 86.7 86.6 86.4 ...
$ smath : num 111 111 111 110 110 ...
$ vol : num 0.335 0.341 0.401 0.402 0.382 ...
$ rsi : num 45.7 43.6 36.6 36.3 34.7 ...
$ dvi : num 0.00968 0.00306 -0.01575 -0.01189 -0.00623 ...
$ nxtCl : num 82.8 79.2 79 78.2 77.4 ...
Any help or insight in getting predict() to work in this instance would be greatly appreciated. Thanks.
Here's some reproducible code. In putting this together, I have 'removed' the error. Unfortunately, although it now works, I am none the wiser as to what was causing the problem before:
require(quantstrat)
require(PerformanceAnalytics)
require(nnet)
initDate <- "2004-09-30"
endDate <- "2010-09-30"
symbols <- c("SPY")
getSymbols(symbols, from=initDate, to=endDate, index.class=c("POSIXt","POSIXct"))
rsi <- RSI(Cl(SPY))
smaTen <- SMA(Cl(SPY))
smaFif <- SMA(Cl(SPY),n=50)
nxtCl <- lag(Cl(SPY),-1)
tmp <- SPY[,-5]
tmp <- tmp[,-5]
miData <- merge(tmp,rsi,smaTen,smaFif,nxtCl)
names(miData) <- c("Op","Hi","Lo","Cl","rsi","smaTen","smaFif","nxtCl")
miData <- miData[50:1512]
scaled.miData <- scale(miData)
miData.train <- as.data.frame(scaled.miData[1:1000])
miData.test <- as.data.frame(scaled.miData[1001:1463])
nn <- nnet(nxtCl ~ .,data=miData.train,linout=T,size=10,decay=0.001,maxit=10000)
preds <- predict(object=nn, miData.test)
