Choosing an Execution Mode

rfastloess provides three execution modes for different data sizes and processing needs:

Execution mode comparison

Mode	Class	Use Case	Memory	Key Features
Batch	`Loess`	Full dataset	Entire dataset	CI, PI, CV
Streaming	`StreamingLoess`	>100K points	Per chunk	With overlap
Online	`OnlineLoess`	Live / real-time	Sliding window	Per-point

Quick Decision Guide

Situation	Mode
Data fits in memory; needs intervals or CV	Batch
Data too large for memory or arrives in chunks	Streaming
Data arrives point-by-point in real time	Online

Batch Adapter

Standard mode for complete datasets. Supports all features.

When to Use

Dataset fits in memory
Need intervals, cross-validation, or diagnostics
Processing complete files

Example

library(rfastloess)

x <- seq(0, 2 * pi, length.out = 100)
y <- sin(x) + rnorm(100, sd = 0.3)

model <- Loess(
    fraction = 0.5,
    iterations = 3,
    parallel = TRUE,
    outputs = "diagnostics",
    intervals = intervals_opts(confidence = 0.95, prediction = 0.95)
)
result <- fit(model, x, y)
cat("95% CI at midpoint: [", result$confidence_lower[50], ", ",
    result$confidence_upper[50], "]\n")
#> 95% CI at midpoint: [ -0.01020224 ,  0.2409727 ]
cat("R2:", result$diagnostics$r_squared, "\n")
#> R2: 0.7848951

Streaming Adapter

Process large datasets in chunks with configurable overlap.

When to Use

Dataset >100,000 points
Memory-constrained environments
Batch processing pipelines

Parameters

Parameter	Default	Description
`chunk_size`	5000	Points per chunk
`overlap`	500	Overlap between chunks
`merge_strategy`	`"weighted_average"`	How to merge overlaps

Merge Strategies

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

Merge Strategies

Example

library(rfastloess)

x <- seq(0, 2 * pi, length.out = 100)
y <- sin(x) + rnorm(100, sd = 0.3)

model <- StreamingLoess(
    fraction = 0.3,
    iterations = 2,
    chunk_size = 5000,
    overlap = 500,
    merge_strategy = "average"
)
process_chunk(model, x, y)
#> <LoessResult>
#>   Points:            0 
#>   Fraction Used:     0.3 
#>   Iterations Used:   2
result <- finalize(model)
cat("Smoothed y[0]:", result$y[1], "\n")
#> Smoothed y[0]: 0.07117552

Always call finalize(): The streaming adapter buffers overlap data. Call finalize(model) after processing all chunks to retrieve the buffered tail.

Online Adapter

Incremental updates with a sliding window for real-time data.

When to Use

Data arrives incrementally (sensors, streams)
Need real-time smoothed values
Fixed memory budget

Online Adapter

Parameters

Parameter	Default	Description
`window_capacity`	1000	Max points in window
`min_points`	2	Points before output starts
`update_mode`	`"incremental"`	Update strategy

Update Modes

Mode	Behavior	Speed
`"incremental"`	Update only affected fits	Faster
`"full"`	Recompute entire window	More accurate

Example

library(rfastloess)

x <- seq(0, 2 * pi, length.out = 100)
y <- sin(x) + rnorm(100, sd = 0.3)

model <- OnlineLoess(
    fraction = 0.2,
    window_capacity = 100,
    min_points = 5,
    update_mode = "incremental"
)
shown <- 0
for (i in seq_along(x)) {
    result <- add_point(model, x[i], y[i])
    if (!is.null(result) && shown < 5) {
        cat(result$y, "\n")
        shown <- shown + 1
    }
}
#> 0.2240037 
#> 0.6589422 
#> 0.8440962 
#> 0.5805868 
#> 0.7859969

Feature Comparison

Feature	Batch	Streaming	Online
Confidence intervals	✓	✗	✗
Prediction intervals	✓	✗	✗
Cross-validation	✓	✗	✗
Diagnostics	✓	✓	✗
Residuals	✓	✓	✓
Robustness weights	✓	✓	✓
Parallel execution	✓	✓	✗

Next Steps

?Loess — All configuration options
Real-Time Processing — Real-time processing guide

sessionInfo()
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