forecast.gts: Forecast a hierarchical or grouped time series
In earowang/hts: Hierarchical and Grouped Time Series

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Methods for forecasting hierarchical or grouped time series.

## S3 method for class 'gts'
forecast(
  object,
  h = ifelse(frequency(object$bts) > 1L, 2L * frequency(object$bts), 10L),
  method = c("comb", "bu", "mo", "tdgsa", "tdgsf", "tdfp"),
  weights = c("wls", "ols", "mint", "nseries"),
  fmethod = c("ets", "arima", "rw"),
  algorithms = c("lu", "cg", "chol", "recursive", "slm"),
  covariance = c("shr", "sam"),
  nonnegative = FALSE,
  control.nn = list(),
  keep.fitted = FALSE,
  keep.resid = FALSE,
  positive = FALSE,
  lambda = NULL,
  level,
  FUN = NULL,
  xreg = NULL,
  newxreg = NULL,
  parallel = FALSE,
  num.cores = 2,
  ...
)

`object`	Hierarchical or grouped time series object of class `{gts}`
`h`	Forecast horizon
`method`	Method for distributing forecasts within the hierarchy. See details
`weights`	Weights used for "optimal combination" method: `weights="ols"` uses an unweighted combination (as described in Hyndman et al 2011); `weights="wls"` uses weights based on forecast variances (as described in Hyndman et al 2016); `weights="mint"` uses a full covariance estimate to determine the weights (as described in Wickramasuriya et al 2019); `weights="nseries"` uses weights based on the number of series aggregated at each node.
`fmethod`	Forecasting method to use for each series.
`algorithms`	An algorithm to be used for computing the combination forecasts (when `method=="comb"`). The combination forecasts are based on an ill-conditioned regression model. "lu" indicates LU decomposition is used; "cg" indicates a conjugate gradient method; "chol" corresponds to a Cholesky decomposition; "recursive" indicates the recursive hierarchical algorithm of Hyndman et al (2016); "slm" uses sparse linear regression. Note that `algorithms = "recursive"` and `algorithms = "slm"` cannot be used if `weights="mint"`.
`covariance`	Type of the covariance matrix to be used with `weights="mint"`: either a shrinkage estimator (`"shr"`) with shrinkage towards the diagonal; or a sample covariance matrix (`"sam"`).
`nonnegative`	Logical. Should the reconciled forecasts be non-negative?
`control.nn`	A list of control parameters to be passed on to the block principal pivoting algorithm. See 'Details'.
`keep.fitted`	If `TRUE`, keep fitted values at the bottom level.
`keep.resid`	If `TRUE`, keep residuals at the bottom level.
`positive`	If `TRUE`, forecasts are forced to be strictly positive (by setting `lambda=0`).
`lambda`	Box-Cox transformation parameter.
`level`	Level used for "middle-out" method (only used when `method = "mo"`).
`FUN`	A user-defined function that returns an object which can be passed to the `forecast` function. It is applied to all series in order to generate base forecasts. When `FUN` is not `NULL`, `fmethod`, `positive` and `lambda` are all ignored. Suitable values for `FUN` are `tbats` and `stlf` for example.
`xreg`	When `fmethod = "arima"`, a vector or matrix of external regressors used for modelling, which must have the same number of rows as the original univariate time series
`newxreg`	When `fmethod = "arima"`, a vector or matrix of external regressors used for forecasting, which must have the same number of rows as the `h` forecast horizon
`parallel`	If `TRUE`, import `parallel` package to allow parallel processing.
`num.cores`	If `parallel = TRUE`, specify how many cores are going to be used.
`...`	Other arguments passed to `ets`, `auto.arima` or `FUN`.

Base methods implemented include ETS, ARIMA and the naive (random walk) models. Forecasts are distributed in the hierarchy using bottom-up, top-down, middle-out and optimal combination methods.

Three top-down methods are available: the two Gross-Sohl methods and the forecast-proportion approach of Hyndman, Ahmed, and Athanasopoulos (2011). The "middle-out" method "mo" uses bottom-up ("bu") for levels higher than level and top-down forecast proportions ("tdfp") for levels lower than level.

For non-hierarchical grouped data, only bottom-up and combination methods are possible, as any method involving top-down disaggregation requires a hierarchical ordering of groups.

When xreg and newxreg are passed, the same covariates are applied to every series in the hierarchy.

The control.nn argument is a list that can supply any of the following components:

ptype: Permutation method to be used: "fixed" or "random". Defaults to "fixed".
par: The number of full exchange rules that may be tried. Defaults to 10.
gtol: The tolerance of the convergence criteria. Defaults to sqrt(.Machine$double.eps).

A forecasted hierarchical/grouped time series of class gts.

In-sample fitted values and resiuals are not returned if method = "comb" and nonnegative = TRUE.

Earo Wang, Rob J Hyndman and Shanika L Wickramasuriya

Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism, International Journal of Forecasting, 25, 146-166.

Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. https://robjhyndman.com/publications/hierarchical/

Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. https://robjhyndman.com/publications/hgts/

Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804–819. https://robjhyndman.com/publications/mint/

Wickramasuriya, S. L., Turlach, B. A., & Hyndman, R. J. (to appear). Optimal non-negative forecast reconciliation. Statistics and Computing. https://robjhyndman.com/publications/nnmint/

Gross, C., & Sohl, J. (1990). Dissagregation methods to expedite product line forecasting, Journal of Forecasting, 9, 233–254.

hts, gts, plot.gts, accuracy.gts

forecast(htseg1, h = 10, method = "bu", fmethod = "arima")

## Not run: 
  forecast(
    htseg2, h = 10, method = "comb", algorithms = "lu",
    FUN = function(x) tbats(x, use.parallel = FALSE)
  )

## End(Not run)