Dear Javad Bayat I think that people on this list has been most helpful to your with your questions about how to use neural networks in R.
Now you have come to the point where you need a more statistical understanding
of your data before you can decide whether neural network methods is really the
best way for you to do forecasting. Let me illustrate that.
I'm doing this in R.
### Rscript start ####
## import your data
bayat <- read.table("bayat.txt", h = TRUE)
## attach the neuralnet package
library("neuralnet")
## fit a simple model
fit <- neuralnet(pH ~ station + month, data = bayat)
## create new data into the future
futuredata <- expand.grid(station = 1:8, month = 21:25)
## do predictions
predictions <- compute(fit, futuredata)
predictions <- data.frame(predictions[["neurons"]][[1]][,2:3], pH =
predictions$net.result)
head(predictions)
station month pH
1 1 21 8.294072681
2 2 21 8.294072683
3 3 21 8.294072684
4 4 21 8.294072685
5 5 21 8.294072685
6 6 21 8.294072686
## plot the results together with original data
library(lattice)
library(latticeExtra)
xyplot(pH ~ month|factor(station), data = bayat, type = "b", layout = c(4,2),
xlim = c(0:26))+
xyplot(pH ~ month|factor(station), data = predictions, type = "b", layout =
c(4,2), col = "red")
## calculate mean pH for each station
aggregate(list(pH = bayat$pH), by = list(station = bayat$station), mean)
station pH
1 1 8.3215
2 2 8.4640
3 3 8.2890
4 4 8.2100
5 5 8.3240
6 6 8.4575
7 7 8.2085
8 8 8.0645
## overall mean of pH
mean(bayat$pH)
[1] 8.292375
### Rscript end ###
Here is my comment to the results.
I have attached the plot as a png file (hope it makes its way to the list). The
blue curve is the original data and red curve is the predictions into the
future. As you can see the levels of pH is somewhat constant during the
measurement periods. The deviations from constant levels may be due solely to
measurement errors.
As you can see from the figure that using the fitted simple neural network to
forecast is not that good. It seem that the neural network forecast the value
of pH to be the overall mean for all stations.
Your're a master student so I would strongly suggest that you consider the
following points together with your supervisor.
1. Make plots of your data(all responses) to see how your data behave and show
the plots to your supervisor.
2. Discuss with your supervisor what kind of underlying processes the data
comes from. Then you can probably make some assumptions on some cyclic behavior
of the data such as a seasonal variation (as month = 1:20 this variable does
not define any seasonality).
3. Is neural network really the method to use here? Discuss with your
supervisor whether there could be other methods from theory on time series
analysis that could be useful.
4. Have a look at the "Task Views" at CRAN
(http://cran.r-project.org/web/views/): see e.g. TimeSeries, MachineLearning,
Environmetrics, Econometrics, Finance.
I do not have time to help you further. If you have further questions please
contact your supervisor.
Yours sincerely / Med venlig hilsen
Frede Aakmann Tøgersen
Specialist, M.Sc., Ph.D.
Plant Performance & Modeling
Technology & Service Solutions
T +45 9730 5135
M +45 2547 6050
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http://www.vestas.com
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> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
> On Behalf Of PIKAL Petr
> Sent: 7. januar 2014 09:01
> To: javad bayat
> Cc: R-help@r-project.org
> Subject: Re: [R] help
>
> Hi
>
> and what is wrong with e.g.
>
> fit <- neuralnet(pH~station+month, data=yourdata)
>
> As I said I am not an expert in neural nets but here is some explanation how
> it works
> http://gekkoquant.com/2012/05/26/neural-networks-with-r-simple-
> example/
>
> based on that after fitting you could do
>
> compute(fit, testdata)
>
> where testdata shall be station and month.
>
> However for time series it can be more appropriate something like ARIMA
> modelling.
>
> Petr
>
> From: javad bayat [mailto:j.bayat...@gmail.com]
> Sent: Monday, January 06, 2014 5:58 PM
> To: PIKAL Petr
> Subject: Re: [R] help
>
> Dear Petr;
> I want to write function that: for example for pH:
> according these 20 months predict the variability of pH for next month and
> stations.
> all best.
>
> On Mon, Jan 6, 2014 at 7:23 PM, PIKAL Petr
> <petr.pi...@precheza.cz<mailto:petr.pi...@precheza.cz>> wrote:
> Hi
> can you be more specific? In what aspect those packages does not comply
> with your data? What did you do for testing it?
>
> I am not an expert in neural networks but I do not see anything which
> prevents using your data in nnet.
>
> Petr
>
>
> > -----Original Message-----
> > From: r-help-boun...@r-project.org<mailto:r-help-bounces@r-
> project.org> [mailto:r-help-bounces@r-<mailto:r-help-bounces@r->
> > project.org<http://project.org>] On Behalf Of javad bayat
> > Sent: Monday, January 06, 2014 3:16 PM
> > To: R-help@r-project.org<mailto:R-help@r-project.org>
> > Subject: Re: [R] help
> >
> > Dear Petr;
> > I saw the nnet and neuralnet packag, and I cant find some thing
> > relating with my data based on neural network.
> >
> >
> > On Mon, Jan 6, 2014 at 10:55 AM, PIKAL Petr
> <petr.pi...@precheza.cz<mailto:petr.pi...@precheza.cz>>
> > wrote:
> >
> > > Hi
> > >
> > > Why you did not use dput for sending data? It is far better than
> > > picture, which can not be used without retyping.
> > >
> > > Redarding neural network, did you try e.g. nnet or neuralnet package.
> > >
> > > Petr
> > >
> > > > -----Original Message-----
> > > > From: r-help-boun...@r-project.org<mailto:r-help-bounces@r-
> project.org> [mailto:r-help-bounces@r-<mailto:r-help-bounces@r->
> > > > project.org<http://project.org>] On Behalf Of javad bayat
> > > > Sent: Monday, January 06, 2014 7:37 AM
> > > > To: R-help@r-project.org<mailto:R-help@r-project.org>
> > > > Subject: Re: [R] help
> > > >
> > > > Dear all;
> > > > many thanks for your answers.
> > > > Hear is my data (not all row: the station was 8 station at 20
> > month)
> > > > which I forward it as image. I hope some one can help me to do
> > > > Neural network for prediction of next month.
> > > > many thanks.
> > > > all bests.
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > --
> > > > Best Regards
> > > > Javad Bayat
> > > > M.Sc. Environment Engineering
> > > > Shahid Beheshti (National) University (SBU) Alternative Mail:
> > > > bayat...@yahoo.com<mailto:bayat...@yahoo.com>
> > >
> >
> >
> >
> > --
> > Best Regards
> > Javad Bayat
> > M.Sc. Environment Engineering
> > Shahid Beheshti (National) University (SBU) Alternative Mail:
> > bayat...@yahoo.com<mailto:bayat...@yahoo.com>
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org<mailto:R-help@r-project.org> mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-
> > guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Best Regards
> Javad Bayat
> M.Sc. Environment Engineering
> Shahid Beheshti (National) University (SBU)
> Alternative Mail: bayat...@yahoo.com<mailto:bayat...@yahoo.com>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.
<<attachment: pH-nn.png>>
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
