LOESS Online Smoothing — OnlineLoess • rfastloess

Create a stateful LOESS model for real-time online data. Maintains a sliding window and processes each incoming point immediately via add_point.

Usage

OnlineLoess(
    fraction = 0.67,
    window_capacity = 1000L,
    min_points = 2L,
    ...,
    iterations = 0L,
    weight_function = "tricube",
    robustness_method = "bisquare",
    scaling_method = "mad",
    boundary_policy = "extend",
    zero_weight_fallback = "use_local_mean",
    update_mode = "incremental",
    auto_converge = NULL,
    intervals = NULL,
    degree = "linear",
    dimensions = 1L,
    distance_metric = "normalized",
    surface_mode = "interpolation",
    weighted_metric_weights = NULL,
    cell = NULL,
    interpolation_vertices = NULL,
    boundary_degree_fallback = NULL,
    missing = "error",
    outputs = NULL
)

Arguments

fraction: Smoothing fraction, greater than 0 and up to 1. Default: 0.67. See Details for guidance on choosing a value.
window_capacity: Maximum number of points kept in the sliding window, at least 3. Default: 1000.
min_points: Minimum number of points required before smoothing begins, between 2 and window_capacity. Default: 2.
...: Not used; forces all subsequent arguments to be named.
iterations: Number of robustness iterations, between 0 and 1000 (inclusive). Default: 3.
weight_function: Kernel weight function. One of "tricube" (default), "gaussian", "uniform" (alias: "boxcar"), "cosine", "epanechnikov", "biweight" (alias: "bisquare"), or "triangle" (alias: "triangular").
robustness_method: Outlier downweighting method: "bisquare" (default; alias: "biweight"), "huber", or "talwar".
scaling_method: Residual scale estimation for robustness weights: "mad" (default; alias: "median_absolute_deviation"), "mar" (alias: "median_absolute_residual"), or "mean" (alias: "mean_absolute_residual").
boundary_policy: Boundary handling strategy: "extend" (default; alias: "pad"), "reflect" (alias: "mirror"), "zero", or "noboundary" (alias: "none").
zero_weight_fallback: Fallback policy when all robustness weights drop to zero: "use_local_mean" (default; aliases: "local_mean", "mean"), "return_original" (alias: "original"), or "return_none" (alias: "none").
update_mode: Window update strategy: "incremental" (default; alias: "single") updates only the newest point; "full" (alias: "resmooth") re-smooths all window points after each addition.
auto_converge: Convergence tolerance for early stopping of robustness iterations. NULL (default) disables early stopping.
intervals: Grouped coverage levels from intervals_opts.
degree: Local polynomial degree: "constant", "linear" (default), "quadratic", "cubic", or "quartic".
dimensions: Number of predictor dimensions; each add_point() call must provide one x coordinate per dimension.
distance_metric: Distance metric for neighbourhood computation: "normalized" (default), "euclidean", "manhattan", "chebyshev", "minkowski", or "weighted". Use "minkowski:p" to set a custom p value.
surface_mode: Surface evaluation mode: "interpolation" (default) or "direct".
weighted_metric_weights: Numeric vector of per-dimension weights. Length must equal dimensions. Only used when distance_metric = "weighted"; setting distance_metric = "weighted" without providing this raises an error. NULL (default) has no effect unless distance_metric = "weighted" is set.
cell: Cell size tuning parameter for the interpolation grid. NULL (default) uses the library default.
interpolation_vertices: Number of vertices in the interpolation grid. NULL (default) uses the library default.
boundary_degree_fallback: Logical; if TRUE, interpolation vertices lying outside the range of the data are fitted with a linear model rather than the requested degree, avoiding unstable extrapolation. It has no effect below quadratic degree, and FALSE reproduces stats::loess(). NULL (default) uses the library default.
missing: Policy for a non-finite (NaN/Inf) x or y value passed to add_point: "error" (default) raises an error, "drop" silently ignores the point (returns NULL) instead of adding it to the window.
outputs: Optional character vector selecting "weights", "gradient" (or "derivative"), and "se". NULL (default) selects no optional components; "se" requires full update mode.

Value

An OnlineLoess object.

Details

Best suited when data arrives incrementally (e.g. sensors or streams), real-time smoothed values are needed, or memory is fixed. For datasets that fit in memory, see Loess; for large batches processed in chunks, see StreamingLoess.

Examples

model <- OnlineLoess(fraction = 0.2, window_capacity = 20)
x <- 1:50
y <- sin(x * 0.1) + rnorm(50, 0, 0.1)
smoothed <- numeric(0)
for (i in seq_along(x)) {
    result <- add_point(model, x[i], y[i])
    if (!is.null(result)) smoothed <- c(smoothed, result$y)
}
head(smoothed, 5)
#> [1] 0.1201261 0.1898465 0.3098642 0.3037980 0.4955887