neural network - R neuralnet does not converge within stepmax for time series -

i'm writing neural network prediction of elements in time series x + sin(x^2) in r, using neuralnet package. how training data being generated, assuming window of 4 elements, , last 1 one has predicted:

nntr0 <- ((1:25) + sin((1:25)^2)) nntr1 <- ((2:26) + sin((2:26)^2)) nntr2 <- ((3:27) + sin((3:27)^2)) nntr3 <- ((4:28) + sin((4:28)^2)) nntr4 <- ((5:29) + sin((5:29)^2)) 

then, turn these data.frame:

nntr <- data.frame(nntr0, nntr1, nntr2, nntr3, nntr4) 

then, proceed train nn:

net.sinp <- neuralnet(nntr4 ~ nntr0 + nntr1 + nntr2 + nntr3, data=nntr, hidden=10, threshold=0.04, act.fct="tanh", linear.output=true, stepmax=100000) 

which, after while, gives me message

warning message: algorithm did not converge in 1 of 1 repetition(s) within stepmax  call: neuralnet(formula = nntr4 ~ nntr0 + nntr1 + nntr2 + nntr3, data = nntr,     hidden = 10, threshold = 0.04, stepmax = 100000, act.fct = "tanh", linear.output = true) 

can me figure out why not converging? many thanks

with tanh activation function (it bounded), difficult reproduce linear trend in signal.

you can use linear activation functions instead, or try detrend signal.

# data dx <- 1 n <- 25 x <- seq(0,by=dx,length=n+4) y <- x + sin(x^2) y0 <- y[1:n] y1 <- y[1 + 1:n] y2 <- y[2 + 1:n] y3 <- y[3 + 1:n] y4 <- y[4 + 1:n] d <- data.frame(y0, y1, y2, y3, y4) library(neuralnet)  # linear activation functions r <- neuralnet(y4 ~ y0 + y1 + y2 + y3, data=d, hidden=10) plot(y4, compute(r, d[,-5])$net.result)  # no trend d2 <- data.frame(   y0 = y0 - x[1:n],    y1 = y1 - x[1 + 1:n],    y2 = y2 - x[2 + 1:n],    y3 = y3 - x[3 + 1:n],    y4 = y4 - x[4 + 1:n] ) r <- neuralnet(y4 ~ y0 + y1 + y2 + y3, data=d2, hidden=10, act.fct="tanh" ) plot(d2$y4, compute(r, d2[,-5])$net.result)