config-i1 / legion Goto Github PK

Things to do:

1. parameters checks,
2. maker,
3. initialiser,
4. name of the model

1. Make it more readable, similar to adam() from smooth, e.g. proper inputs and outputs.
2. Rename some functions, so that they are easier to read.

This should include:

1. plot.oves
2. forecast.oves

Take the E(x) = 1 into account, when estimating the models

Redo this, taking that the mean is different

The function that does model selection for ves. This can be done in a very wide sense, selecting components and then checking if grouped, dependent or individual parameters are needed

This is a followup thing based on Svetunkov&Boylan research. The idea is to model occurrence variables in groups.

Things to do:

1. Proper prediction intervals for ives / ivets;
2. Likelihood calculation;
3. Information criteria;
4. Automatic selection of the type of model in oves() / ovets().

Create state-space VARMA with order selection, blackjack and hookers.

Implement the simulate method for VETS. As simple as that. In order to do that, we need sim.vets() function.

Make explicit maximisation of likelihood or minimisation of a different loss, including the custom one.

This should accept "PPP", "XXX", "YYY" and "FFF". The default one should be "PPP".

Exogenous means including those that are not in Yt vector.
Non-lagged means model of a type Yt = A Yt + Et,
where A has zeroes on diagonal.

This includes:

1. forecast, predict,
2. rstandard, rstudent,
3. outlierdummy,
4. plot, similar to plot.greybox and plot.smooth,

In the best case these should include:

1. "conditional" - elliptic intervals for each j. These are correct, but are very hard to evaluate and return. Their usefulness is questionable.
2. "unconditional" - rectangular regions for each separate series for each j=1,..,h. These are just projections of the max and min values of ellipses on separate axes. These are incorrect but could be useful;
3. "independent" - assuming independence between series and based on variances for each separate series. These are even less correct than (2), but still useful.
   The simplest one is (3). It is already available in ves() and vets(). The hardest one is (1).

One of the options - produce general class from ves() / vets() and create functions that would extract and plot things.
For example:

ourModel <- ves(Y, intervals="conditional", level=0.95)
plotEllipse(ourModel, h=1)

ourModel$bounds would contain matrix with upper and lower bounds of y_1, y_2, y_3 etc conditional on a value of y_k (could be a large matrix). In case with "unconditional" or "independent" this thing would simplify to a matrix with one row.
plotEllipse() function would plot all the ellipses for h=1.

Implement several seasonal components, similar to adam() from smooth. The parameter lags is needed for this.

This should involve quantiles of multivariate normal distribution (isolines) in order to mark residuals as outliers. Currently it works in a univariate style.

Hi config-i1,

thanks for your package! I like it. However, I ran into a little bug today. We can reproduce that easily using the Example code:

library(legion)
Y <- ts(cbind(
    1000 + 0.5 * c(1:100) + rnorm(100, 0, 10),
    cbind(1000 + 1.5 * c(1:100) + rnorm(100, 0, 10))
),
frequency = 12
)
ves(Y, h = 2, holdout = TRUE, silent = FALSE)
ves(Y, h = 1, holdout = TRUE, silent = FALSE)

The second call to ves (the one with h=1) throws errors with the following message:

Error in `colnames<-`(`*tmp*`, value = dataNames) : 
  attempt to set 'colnames' on an object with less than two dimensions

I guess that there is an object that becomes a vector if h=1 and therefore looses its colnames attribute.

Maybe you can look into that. Or provide some guidance on how to fix it myself. I'd be more than happy to open a PR.

Thanks in advance
BerriJ

This should do selection based on the following steps:

1. Apply VETS(Z,Z,Z)PIC(N,N,N)
2. Select VETS(,,*)PIC(Z,Z,Z):

• Select P,
• Select I,
• Select C, based on I (if it is individual, don't try it).