My lack of understanding of R makes me freeze in my work and seek help. I am looking to build a neural network from some time series data, and then build a prediction using separate data and a model returned by a trained neural network.
I created xts containing the dependent variable nxtCl (one-day direct closing price of shares) and independent variables (a set of relevant prices and technical indicators).
I split xts into two, one set is training data, and the other set for testing / forecasting is miData.train and miData.test respectively. Subsequently, I changed these two xts to scalable data frames.
miData.train <- scale(as.data.frame(miData.train)) miDate.test <- scale(as.data.frame(miData.test))
Using the nnet package I can build a neural network from training data:
nn <- nnet(nxtCl ~ .,data=miData.train,linout=T,size=10,decay=0.001,maxit=10000)
The output of str() for this return object of the formula:
> 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"
Then I try to run the prediction function using this nn model, and the data that I saved separately 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 there are no attributes in my data frame:
terms.default(miData.test) Error in terms.default(miData.test) : no terms component nor attribute
but that is why the prediction will not be fulfilled?
miData.test has names matching the terms 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 exactly matches the data that was used to build nn in the first place. I tried adding my own named attributes to miData.test , matching nn conditions, but that didn't work. 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 case would be greatly appreciated. Thanks.
Here is some reproducible code. Combining this, I “deleted” the error. Unfortunately, although it works now, I do not think it was 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)