I am using Rob Hyndman forecasting with STL
fit <- stl(USAccDeaths,s.window="periodic")
forecast(fit)
I am trying to get forecasted numbers (don't want to plot), so I just used forecast(fit), but I couldn't find source for forecast(), there are lot of forecast functions in forecast package source files. I need to understand how these numbers are calculated to match with other implementation I have.
Can someone please help me in pointing to which part of the code will be executed? That helps me in continuing my debugging.
EDIT:
My current understanding is, forecast.R --->forecast.ts will be called and from there
stlf(object,h=h,level=level,fan=fan,lambda = lambda, ...)
should be invoked. Am I correct? If so, where is the source for stlf function located?
I don't think that is correct. If you run the example in ?forecast which is what you are showing, then the 'fit'-object is of class-"stl" and so forecast.stl is the underlying function that is called.
stlf is in the namespace of pkg:forecast. Just type the function name (without quotes) at the console.
Related
I am looking at this code, previously v-transformations were done and fitting VT-ARMA copula models, now here it is applying shapiro test to residuals and want to plot 4 graphs:
https://i.stack.imgur.com/gTtBU.png
These 4 plots should come out of plot(vtcop, plotoption=3) etc... I have never used this argument plotoption, i think this argument is contained in the tscopula package ,but I have already done the necessary research on the help and read the pdf file that explains the tscopula package but there is no such "plotoption".
Can anyone tell me why it tells me unused argument at this point?
This code from by paper of AlexanderMcNeil: "Modelling Volatile Time Series with V-Transforms and Copulas".
Thank you very much. Good day.
I am learning R package SimInf to simulate data-driven stochastic epidemiological models. As I was reading the documentation I came across an unrecognized funcion Nn when defining a function for epicurves. Specifically, this line:
j <- sample(seq_len(Nn(model)), 1)
Values of model are integers. My guess is that Nn selects non-negative values, however my R does not recognize this function. From documentation it does not look like they pre-defined Nn either. Can someone please tell if they know what "Nn" is for? Thank you.
A way to go is always taking the package-name and triple-":" it, such that you can find nearly all functions inside the package. Maybe you are familiar with namespacing a function via packageName::functionFrompackageTocall. The packageName::: shows (nearly) all functions defined in this package. If you do this in R-Studio with SimInf:: and SimInf:::, you will see that the latter gives much more functions. But you can only find the functions SimInf:::Nd and SimInf:::Nc, not the Nn-function. Hence you will have to go to the github-sources of the package, in this case https://github.com/stewid/SimInf .Then search for Nn the whole repository. You will see that it seems like it is always an int, but this doesn't help you since you want to get ii as a function, not as a variable. Scrolling further down in the search-results, you will find the NEWS.md-file which mentions The 'Nn' function to determine the number of nodes in a model has been replaced with the S4 method 'n_nodes'. in the https://github.com/stewid/SimInf/blob/fd7eb4a29b82a4a97f64b528bb0e78e5474aa8a5/NEWS.md file under SimInf 8.0.0 (2020-09-13). Hence having a current version of SimInf installed, it shouldn't use the method Nn anymore. If you use it in your code, replace it by n_nodes. If you find it in current package code, you can email the package-maintainer that you found a bug in his code.
TLDR: Nn is an outdated version of n_nodes
I have used the function hw to analyze a time series
fcast<-hw(my_series,h=12,level=95,seasonal="multiplicative",damped=TRUE,lambda=NULL)
By looking at the fcast$model$par I observe the values of alpha, beta, gamma, phi, and the initial states.
I've also looked at the contents of fcast$model$states to see the evolution of all the values. I've tried to reproduce the results in Excel in order to understand the whole procedure.
To achieve the same values of b (trend) as in fcast$model$states I observe that I have to use a formula like the one in the bibliography about the Holt-Winters method:
b(t)=beta2*(l(t)-l(t-1)+(1-beta2)*phi*b(t-1)
But, if in fcast$model$par beta=0.08128968, I find that in order to achieve the same results I have to use beta2=0.50593541.
What's the reason for that? I don't see any relationship between beta and beta2.
I have also found that in order to get the same forecast as the one obtained with the hw function I have to use the following formulas once the data are finished:
l(t)=l(t-1)+b(t-1)
b(t)=phi*b(t-1)
^y(t)=(l(t-1)+b(t-1))*s(t-m)
I haven't found any bibliography on this forecasting phase, explaining that some parameters are no longer used. For instance, in this case phi is still used for b(t), but not used anymore for l(t).
Can anyone refer to any bibliography where I can find this part explained?
So in the end I've been able to reproduce the whole set of data in Excel, but there's a couple of steps I would like to understand better.
Thank you!!
I want to follow the tutorial found on this site, but despite being thorough in all other aspects, the author has not included information on what packages need to be used for the code to function.
As far as I understand one of them will be the PerformanceAnalytics package, yet my inexperienced eye is not sure about what else I will need to include.
The fapply function used in the code is one example that I cannot find.
fapply()
Error: could not find function "fapply"
library(sos)
findFn("fapply", sortby = "Function")
The findFN(...) function is great. It should open an internet browser window with the search results by itself at least it does for me.
The tutorial on Backtesting a Trading Strategy uses time series data as seen its part 1 and part 2. fapply is also used in part 2
As the data being collected and processed is time-series data, fapply() function belongs to far package which is used for Modelization for Functional AutoRegressive Processes.
I hope this helps.
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.