R: Tidying methods for Nural Network Time Series models

tidiers_nnetar {sweep} | R Documentation

## Tidying methods for Nural Network Time Series models

### Description

These methods tidy the coefficients of NNETAR models of univariate time series.

### Usage

    ## S3 method for class 'nnetar'
    sw_tidy(x, ...)

    ## S3 method for class 'nnetar'
    sw_glance(x, ...)

    ## S3 method for class 'nnetar'
    sw_augment(x, data = NULL, timetk_idx = FALSE, rename_index = "index", ...)

### Arguments

`x` | An object of class "nnetar"
`...` | Additional parameters (not used)
`data` | Used with `sw_augment` only. `NULL` by default which simply returns augmented columns only. User can supply the original data, which returns the data + augmented columns.
`timetk_idx` | Used with `sw_augment` only. Uses a irregular timetk index if present.
`rename_index` | Used with `sw_augment` only. A string representing the name of the index generated.

### Value

`sw_tidy()` returns one row for each model parameter, with two columns:

- `term`: The smoothing parameters (alpha, gamma) and the initial states (l, s0 through s10)

- `estimate`: The estimated parameter value

`sw_glance()` returns one row with the columns

- `model.desc`: A description of the model including the three integer components (p, d, q) are the AR order, the degree of differencing, and the MA order.

- `sigma`: The square root of the estimated residual variance

- `logLik`: The data's log-likelihood under the model (`NA`)

- `AIC`: The Akaike Information Criterion (`NA`)

- `BIC`: The Bayesian Information Criterion (`NA`)

- `ME`: Mean error

- `RMSE`: Root mean squared error

- `MAE`: Mean absolute error

- `MPE`: Mean percentage error

- `MAPE`: Mean absolute percentage error

- `MASE`: Mean absolute scaled error

- `ACF1`: Autocorrelation of errors at lag 1

`sw_augment()` returns a tibble with the following time series attributes:

- `index`: An index is either attempted to be extracted from the model or a sequential index is created for plotting purposes

- `.actual`: The original time series

- `.fitted`: The fitted values from the model

- `.resid`: The residual values from the model

### See Also

`nnetar()`

### Examples

    library(dplyr)
    library(forecast)
    library(sweep)

    fit_nnetar <- lynx %>%
        nnetar()

    sw_tidy(fit_nnetar)
    sw_glance(fit_nnetar)
    sw_augment(fit_nnetar)

[Package sweep version 0.2.3 Index]