LOWESS Streaming Smoothing — StreamingLowess • rfastlowess

Create a stateful LOWESS model for streaming data. Processes data in fixed-size chunks with configurable overlap: results for each chunk are returned by process_chunk, and finalize flushes any remaining buffered points after the last chunk.

Usage

StreamingLowess(
    fraction = 0.67,
    ...,
    iterations = 3L,
    weight_function = "tricube",
    robustness_method = "bisquare",
    delta = NULL,
    zero_weight_fallback = "use_local_mean",
    boundary_policy = "extend",
    scaling_method = "mad",
    auto_converge = NULL,
    missing = "error",
    chunk_size = 5000L,
    overlap = NULL,
    merge_strategy = "weighted_average",
    parallel = TRUE,
    outputs = NULL,
    intervals = NULL,
    seed = NULL
)

Arguments

fraction: Smoothing fraction, greater than 0 and up to 1. Default: 0.67.
...: 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".
delta: Interpolation distance threshold, as a non-negative fraction of the x range; points within delta of each other on x share the same local fit. NULL (default) sets it automatically to 1/100th of the x range.
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").
boundary_policy: Boundary handling strategy: "extend" (default; alias: "pad"), "reflect" (alias: "mirror"), "zero", or "noboundary" (alias: "none").
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").
auto_converge: Convergence tolerance for early stopping of robustness iterations. NULL (default) disables early stopping.
missing: Policy for non-finite (NaN/Infinity) values in input data: "error" (default) raises an error, "drop" silently removes affected observations before fitting.
chunk_size: Number of data points per processing chunk, at least 10. Default: 5000.
overlap: Number of overlapping points between consecutive chunks, less than chunk_size. NULL (default) computes chunk_size / 10, clamped to at least 1 and less than chunk_size.
merge_strategy: Strategy for reconciling overlapping chunk regions: "weighted_average" (default; alias: "weighted"), "average" (alias: "mean"), "take_first" (alias: "first"), or "take_last" (alias: "last").
parallel: Logical; enable parallel processing. Default: TRUE.
outputs: Streaming output choices: "se" (standard errors), "diagnostics", "residuals", "weights" (robustness weights), and/or "derivative". NULL (default) returns only the core result.
intervals: Interval options, created with intervals_opts (or a named list with any of confidence, prediction, bootstrap): e.g. intervals = intervals_opts(confidence = 0.90, prediction = 0.99, bootstrap = 200). Confidence and prediction coverage levels are independent and may differ. NULL (default) disables intervals.
seed: Non-negative whole-number seed shared by cross-validation and bootstrap resampling for reproducible results. When cross-validation is unavailable, it controls bootstrap resampling only. NULL (default) uses a random seed.

Value

A StreamingLowess object.

Details

Best suited for datasets over 100,000 points, memory-constrained environments, or batch processing pipelines. For smaller datasets that fit in memory, see Lowess; for point-by-point real-time data, see OnlineLowess. When "diagnostics" is selected, residual_sd is the cumulative sample standard deviation of emitted residuals.

Confidence and prediction interval coverage levels are independent.

Overlapping regions between chunks are reconciled via merge_strategy:

Strategy	Alias	Behavior
"weighted_average" (default)	"weighted"	Distance-weighted blend
"average"	"mean"	Average overlapping values
"take_first"	"first"	Keep left chunk values
"take_last"	"last"	Keep right chunk values

Examples

x <- seq(0, 10, length.out = 100)
y <- sin(x) + rnorm(100, 0, 0.1)
model <- StreamingLowess(fraction = 0.2, chunk_size = 50)
res1 <- process_chunk(model, x[1:50], y[1:50])
res2 <- process_chunk(model, x[51:100], y[51:100])
finalize(model)
#> <LowessResult>
#>   Points:            5 
#>   Fraction Used:     0.2