I am trying to fit a transfer function model using R in order to apply the fitted model to a validation set of data, because SPSS doesn't allow me to (or I don't know how to) compute point forecasts just like the function Arima() from forecast package does. It does let me apply the model, but it does not use the dependet variable's lagged values, that's why I am trying R.
Anyone know how I could get those type of "updated" or validation forecasts using the arimax() function? I am not looking for the following type of predictions:
predict(vixari011, n.ahead=12)
But rather these:
Arima(test$VIX, model = vixari)
From what I have been reading there is no prediction function for the arimax() function, any ideas about how I could forecast to evaluate point-by-point performance? I can just think of computing manually using a spreadsheet...
I had the same problem. I know this post is old but this can help someone.
I used this it worked just fine
forecast(fitted(arimax_ts_model), h=11)
Related
I need to call a forecast model from R within Anlylogic, and return the resulting outputs in R. It is a specific timeseries that I have built in R, and just copying the coefficients to Anylogic is not efficient. I have seen a couple of older posts on similar questions, but I am not sure I can follow. Any advice would be very appreciated.
I have a regression forecast model that uses predictors to provide a forecast along with Prediction Intervals. I need these outputs to be updated by the different values of the predictors and then used in Anylogic.
Suppose I fit AR(p) model using R arima function from stats package. I fit it using a sample x_1,...,x_n. In theory, when predicting x_{n+1} using this model, it needs an access x_n,...x_{n-p}.
How does the model know which observation I want to predict? What if I wanted to actually predict x_n based on x_{n-1},...,x_{n-p-1} and how my code would differ in this case? Can I make in-sample forecasts, similar to Python's functionality?
If my questions imply that I think about forecasting in a wrong way, please kindly correct my understanding of the subject.
Hi I am a newbie in Depp learning fields.
I ran a neural network model (regression) with 2 hidden layers in R (neuralnet Package). then I used the the compute function to get the predicted probabilities.Now I want to regenerate predicted output using the equation used in the neural net. for example, following are weights received from the model object
Intercept.to.1layhid1 4.55725020215
Var1.to.1layhid1 -13.61221477737
VAr2.to.1layhid1 0.30686384857
var1.to.1layhid2 0.23527690062
var2.to.1layhid2 0..67345678
1layhid.1.to.target 1.95414397785
1layhid.2.to.target 3.68009136857
Can any one help me derive a equation with the above weights so that I can replicate the output
Thanks
In order to get the output for new data, you can always use predict function using the fitted model, which is the returned object from neuralnet function.
For instance, if your model is the following:
neuralFit = neuralnet(trainData)
Then you reproduce the output with the following:
predict(neuralFit,newdata)
Otherwise, you'll need to compute the result manually. But you need to understand your network architecture first.
I am working on making a prediction in R using time-series models.
I used the auto.arima function to find a model for my dataset (which is a ts object).
fit<-auto.arima(data)
I can then plot the results of the prediction for the 20 following dates using the forecast function:
plot(forecast(fit,h=20))
However I would like to add external variables and I cannot do it using forecast because it is kind of a black box to me as I am new to R.
So I tried to mimic it by using the arima.sim function and a problem arose:
HOW TO INITIALIZE THIS FUNCTION ?
I got the model by setting model=as.list(coef(fit)) but the other parameters are still obscure to me.
I went through hundreds of page including in stackoverflow but nobody seems to really know what is going on.
How is it calculated ? Like why does n.start (the burn-in period) must have ma+ar length and not only a max(ar,ma) length ? What is exactly start.innov?
I thought I understood when there is only an AR part but I cannot reproduce the results with an AR+MA filter.
My understanding as for the AR is concerned is that start.innov represent the errors between a filtered zero-signal and the true signal, is it true ?
Like if you want to have an ar of order 2 with initial conditions (a1,a2) you need to set
start.innov[1]=a1-ar1*0-ar2*0=a1
start.innov[2]=a2-ar1*start.innov[1]
and innov to rep(0,20) but what to do when facing an arima function how do you set the innov to get exactly the same curbs as forecast does ?
thanks for your help !!!
You seem to be confused between modelling and simulation. You are also wrong about auto.arima().
auto.arima() does allow exogenous variables via the xreg argument. Read the help file. You can include the exogenous variables for future periods using forecast.Arima(). Again, read the help file.
It is not clear at all why you are referring to arima.sim() here. It is for simulating ARIMA processes, not for modelling or forecasting.
Does anyone here know how I can specify additional external variables to an ARIMA model ?
In my case I am trying to make a volatility model and I would like to add the squared returns to model an ARCH.
The reason I am not using GARCH models, is that I am only interested in the volatility forecasts and the GARCH models present their errors on their returns which is not the subject of my study.
I would like to add an external variable and see the R^2 and p-values to see if the coefficient is statistically significant.
I know that this is a very old question but for people like me who were wondering this you need to use cbind with xreg.
For Example:
Arima(X,order=c(3,1,3),xreg = cbind(ts1,ts2,ts3))
Each external time series should be the same length as the original.