multiview_embedding_original | R Documentation |
See Andy's ‘multiview_embedding()' for what he’s using now.
multiview_embedding_original(
data,
response,
lags,
index = 50L,
buffer = 10L,
window = integer(0),
metric = "rmse",
beyond = FALSE,
weight = NULL,
n_weight = ceiling(sqrt(2^length(unlist(lags)))),
cores = NULL
)
data |
[matrix()] or [data.frame()] with named [numeric()] columns |
response |
[character()] column name of the response variable in
|
lags |
[list()] of a named vector of lags for each explanatory variable. |
index |
[integer()] |
buffer |
[integer()] number of forecasts prior to |
window |
[integer()] forecast metric moving window width |
metric |
[character()] |
beyond |
[logical()] |
weight |
TBD |
n_weight |
[integer()] |
cores |
[integer()] number of cores for parallel computation. |
From Luke's email (TODO tidy up when done): Good to hear from you! Yes, what you describe is the main idea behind multiview embedding (MVE). It uses the '2^N - 1' subsets of the superset state space reconstruction of covariates and covariate lags, giving all possible embedding dimensions and embedding combinations that you're looking for. [Andy: Ye and Sugihara 2016 exclude those that have no 0 lag variables, since presumably they're just shifted - keep in for now and check results agree maybe]. Andy asked: is single-view embedding just standard EDM, and Luke said: I think MVE was originally a generalization of S-mapping [Andy: no, for MVE they just use the single nearest neighbour in each view], but we've implemented it as a generalization of EDM. So in our case, single-view embedding would be EDM, but other authors might hear 'single-view embedding' and think 'S-mapping'. Worth double-checking because this is just from memory ;) Andy: TODO don't think that's quite right, checking the code. TODO TODO TODO check the definitions; stick with mve for now to get it all working.
[list()]
Luke A. Rogers
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