Use Case: Real-Time Processing

Streaming and online LOWESS for live data.

Overview

When data arrives continuously—from sensors, logs, or streaming pipelines—you need incremental smoothing that doesn’t require reprocessing the entire dataset.

Online Mode: Point-by-Point

For true real-time applications where each point must be processed immediately.

window_capacity = 25 limits the internal buffer to the 25 most recent observations; each add_point() call costs O(window) rather than growing with total history. min_points = 5 suppresses output until the window holds enough points for a stable fit — calls made before that threshold return NULL. update_mode = "incremental" re-fits only the most recent point rather than the full window, halving typical latency at a modest accuracy cost.

Sensor Data Example

library(rfastlowess)

model <- OnlineLowess(
    fraction = 0.3,
    window_capacity = 25,
    min_points = 5,
    update_mode = "incremental"
)

# Simulate real-time data arrival
count <- 0
for (i in 0:99) {
    yi <- 20.0 + 5.0 * sin(i / 10.0) + sin(i * 1.7) * 0.5

    result <- add_point(model, i, yi)
    if (!is.null(result)) {
        if (count < 5) cat(sprintf("Time %d: smoothed = %.4f\n", i, result$y))
        count <- count + 1
    }
}
#> Time 4: smoothed = 22.1941
#> Time 5: smoothed = 22.7964
#> Time 6: smoothed = 22.4733
#> Time 7: smoothed = 22.9120
#> Time 8: smoothed = 24.0164
cat(sprintf("... (%d more)\n", count - 5))
#> ... (91 more)

Streaming Mode: Chunk Processing

For large datasets that arrive in batches or files.

chunk_size controls how many data points are processed in one pass; matching it to your file-read buffer or message-batch size avoids unnecessary copying. overlap retains that many points from the previous chunk as context so the neighbourhood at chunk boundaries is not artificially truncated. merge_strategy = "weighted_average" blends the overlapping region smoothly; use "last" if chunk boundaries are guaranteed to be well separated and no blending is needed.

Always call finalize(): The streaming adapter buffers overlap data. Call finalize() after the last chunk to retrieve the buffered tail.

Log File Processing

library(rfastlowess)

chunk1_x <- 0:49
chunk1_y <- sin(chunk1_x) + 0.1
chunk2_x <- 50:99
chunk2_y <- sin(chunk2_x) + 0.1

model <- StreamingLowess(
    fraction = 0.1,
    iterations = 2,
    chunk_size = 50,
    overlap = 10,
    merge_strategy = "weighted_average"
)

# Process chunks as they arrive
process_chunk(model, chunk1_x, chunk1_y)
#> <LowessResult>
#>   Points:            40 
#>   Fraction Used:     0.1 
#>   Iterations Used:   2
process_chunk(model, chunk2_x, chunk2_y)
#> <LowessResult>
#>   Points:            50 
#>   Fraction Used:     0.1 
#>   Iterations Used:   2

# CRITICAL: Get buffered overlap data
result <- finalize(model)
cat(sprintf("y[0]: %.6f\n", result$y[1]))
#> y[0]: 0.518853

Real-Time Dashboard Example

The dashboard pattern uses a plain LOWESS fit on a manually managed sliding window rather than OnlineLowess. This is the simplest approach when your UI framework already owns the data buffer and you only need the most recent smoothed value per frame. The trade-off is a full O(window^2) refit on every tick; for high-frequency streams prefer OnlineLowess with update_mode = "incremental" to bound per-frame cost.

library(rfastlowess)

n <- 100
x <- seq(0, 2 * pi, length.out = n)
y <- sin(x) + 0.1

window_x <- numeric(0)
window_y <- numeric(0)
last_smoothed <- 0

for (i in seq_along(x)) {
    window_x <- c(window_x, x[i])
    window_y <- c(window_y, y[i])

    if (length(window_x) > 50) {
        window_x <- window_x[-1]
        window_y <- window_y[-1]
    }

    if (length(window_x) < 2) next
    model <- Lowess(fraction = 0.4)
    result <- fit(model, window_x, window_y)
    last_smoothed <- result$y[length(result$y)]
}
cat(sprintf("Smoothed (dashboard, latest tick): %.4f\n", last_smoothed))
#> Smoothed (dashboard, latest tick): -0.0678

Choosing Parameters

Online Mode

Parameter	Guidance
`window_capacity`	Enough history for `fraction` to work
`min_points`	2–5 typically; higher for stability
`update_mode`	`"incremental"` for speed, `"full"` for accuracy

Streaming Mode

Parameter	Guidance
`chunk_size`	Balance memory vs. processing overhead
`overlap`	10–20% of chunk_size for smooth transitions
`merge_strategy`	`"weighted_average"` for quality, `"average"` otherwise

Performance Considerations

Mode	Memory	Latency	Use Case
Online	Fixed (window)	~1ms/point	Sensors, dashboards
Streaming	~chunk_size	~100ms/chunk	Large files, ETL
Batch	Full dataset	N/A	Analysis, reports

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.5 LTS
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#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
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#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
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#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
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#> [1] rfastlowess_5.0.0
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#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39     desc_1.4.3        R6_2.6.1          fastmap_1.2.0    
#>  [5] xfun_0.61         cachem_1.1.0      knitr_1.52        htmltools_0.5.9  
#>  [9] rmarkdown_2.32    lifecycle_1.0.5   cli_3.6.6         sass_0.4.10      
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#> [17] compiler_4.6.1    tools_4.6.1       ragg_1.5.2        bslib_0.12.0     
#> [21] evaluate_1.0.5    yaml_2.3.12       otel_0.2.0        jsonlite_2.0.0   
#> [25] rlang_1.3.0       fs_2.1.0          htmlwidgets_1.6.4