R: Tidying methods for STL (Seasonal, Trend, Level)...

tidiers_stl {sweep} | R Documentation

## Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series

### Description

Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series

### Usage

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

    ## S3 method for class 'stl'
    sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)

    ## S3 method for class 'stlm'
    sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)

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

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

### Arguments

`x` | An object of class "stl"
`...` | Not used.
`timetk_idx` | Used with `sw_tidy_decomp`. When `TRUE`, uses a timetk index (irregular, typically date or datetime) if present.
`rename_index` | Used with `sw_tidy_decomp`. A string representing the name of the index generated.
`data` | Used with `sw_augment` only.

### Value

`sw_tidy()` wraps `sw_tidy_decomp()`

`sw_tidy_decomp()` 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

- `season`: The seasonal component

- `trend`: The trend component

- `remainder`: observed - (season + trend)

- `seasadj`: observed - season (or trend + remainder)

`sw_glance()` returns the underlying ETS or ARIMA model's `sw_glance()` results 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

- `AIC`: The Akaike Information Criterion

- `BIC`: The Bayesian Information Criterion

- `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

`stl()`

### Examples

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

    fit_stl <- USAccDeaths %>%
        stl(s.window = "periodic")

    sw_tidy_decomp(fit_stl)

[Package sweep version 0.2.3 Index]