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cvts? #63
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A vignette for Until this lands on CRAN, hopefully these clarification will help:
One of the most interesting things to do is run
Not sure what you mean about logarithms appearing everywhere. |
I wonder if we should add the characteristics of the cvts call itself to the cvts object. Was the object a rolling fit? What was the maxHorizon? What was the windowSize? etc. |
Good idea, I'll save those in the object. |
is it possible for you to clarify a couple of terms for all those that do not understand what the algorithm does? for example 1)what are folds? 2)what is a rolling fit (and consequently what is windowsize and maxhorizon) ? a description of the algorithm would be very helpful(I bet you can explain in a couple of paragraphs all of those but it is missing from the manual) |
also related seems to be the cv.errors in forecastHybrid...what is the statistic according to which different models are weighted?is it rolling or for the whole series? |
@savvaskef the documentation appears plenty clear enough. Regardless, here is some clarification. Edit: apologies, looks like the documentation was edited recently Regarding folds, this is not as relevant to time series cross validation and is only meant to serve as an analogy to regular cross validation (non time-series data). In non time-series data, if you perform k-fold cross validation, you will split the dataset into k partitions or folds. For each fold, you will train the model on the other folds and test on the current fold. As an example, let us say you are performing 5 fold cross validation. Then the dataset will be split into folds 1-5. Fold 1 will be held out and folds 2-5 will be used to fit the model. This process will be repeated for fold 2-5. For fold 2, folds 1, 3, 4, 5 will be used for model fitting. In non time series data, each of the rows or cases or independent of each other and can thus be sampled independently. This is however, not the case for time series models. Model fitting for time series depends on the observations being sequential. In order to get an idea of the generalization performance of a time series model, the sampling has to be in line with the model fitting procedure. Two ways to do this are to use rolling (or sliding) windows and non-rolling windows. You can read up about it here. It also has nice diagrams to help you understand the procedures. Regarding horizons, this is standard forecasting terminology. A simple google search led me to this link. I suggest you spend some time reading up on time series since you seem to have some very basic questions. |
@savvaskef Glad to get feedback on the clarity of this function and how to improve it. When writing the documentation one of my concerns was that it may not be clear to others. I think a vignette could help here a lot, particularly one that includes the type of graphics in Rob's blog post. I'll add this to the package roadmap. @ganesh-krishnan is right that you should probably read up on the terminology outside of just the |
Related #66 |
@dashaub If you are writing a vignette for It seems obvious that there should be a separate function to cross validate a hybrid model after it is fit. Am I missing something???? I know that there is the General comment: your package is very good and very useful. Generally the documentation is good (at least better than most). But like nearly all R packages, even better documentation is, by far, the best way to improve usability and popularity. |
where can i find a tutorial/vignette/example about cvts?I know there is an example in the manual but i did nor understand how to use cvmod1 (fully packed with properties)constituents.Can you provide a link or an example.And a request on behalf of wide range of i would like to suggest a methodology. Why not building an example from easy to complex progressively adding parameters for the functions and comments on background needed(for illustration I am not very comfortable with logarithms and they appear as parametes everywhere...shouldn't there be a short definition of how they ae used , ie their properties related to the example)
Thnx again on behalf of may "students"
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