laggedData: Create Input Matrix and Output Vector for Time Series...
In sisal: Sequential Input Selection Algorithm

Description Usage Arguments Details Value Author(s) Examples

Given a time series vector, produces the input matrix and output vector for a time series prediction task. The other parameters are the lags to include and the number of steps ahead to predict.

1	laggedData(x, lags = 0:9, stepsAhead = 1)

`x`	an `atomic` `vector` representing a (uniformly sampled) time series. Any attributes are ignored.
`lags`	which lags to use for prediction. A `vector` of non-negative integral values.
`stepsAhead`	how many steps ahead to predict. A non-negative integral value (`integer` or `numeric`).

The default parameters correspond to predicting one step ahead (position t+1) using the ten most recent values (positions t ... t-9).

A list with two components:

`X`	The `(length(x) - max(lags) - stepsAhead)` rows by `length(lags)` columns input `matrix` with the same type as `x`.
`y`	The output `vector` with `length(x) - max(lags) - stepsAhead` elements. Same type as `x`.