3. Causal decomposition: the rLifting advantage

Most wavelet packages in R are designed for offline analysis: they assume the entire signal is available at once. To perform causal filtering (where the output at time \(t\) depends only on data at times \(0 \dots t\)) with these packages, one must implement a sliding-window loop, re-calculating the transform at every new point.

This approach has \(O(N \cdot W)\) complexity (where \(W\) is the window size). rLifting implements a specialized causal mode using a ring-buffer architecture, which updates the transform state in \(O(N)\) (amortized \(O(1)\) per point).

As in vignette 2, the results are pre-computed by the script data-raw/generate_vignette_data.R.

1. Speed and accuracy: optimized vs. naive

We compare rLifting::denoise_signal_causal against a naive sliding-window implementation using wavethresh. Both methods process a HeaviSine signal (\(n = 500\), \(\sigma = 0.3\)) using the Haar wavelet.

library(rLifting)

if (!requireNamespace("knitr", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
  message("Required package 'knitr' is missing. Vignette code will not run.")
} else {
  library(knitr)
}
#> Warning: package 'knitr' was built under R version 4.4.3

data("benchmark_causal", package = "rLifting")

df_causal = data.frame(
  Method = c("rLifting (causal)", "Naive loop (wavethresh)"),
  Time_ms = c(benchmark_causal$rLifting_Time_Avg * 1000,
              benchmark_causal$Wavethresh_Naive_Time * 1000),
  MSE = c(benchmark_causal$rLifting_MSE,
          benchmark_causal$Wavethresh_Naive_MSE)
)

kable(df_causal,
      col.names = c("Method", "Time (ms)", "MSE"),
      digits = 4,
      caption = "Causal denoising: speed and reconstruction accuracy.")

Causal denoising: speed and reconstruction accuracy.
Method	Time (ms)	MSE
rLifting (causal)	3.7601	0.1094
Naive loop (wavethresh)	808.0936	0.2218

rLifting is approximately 215× faster than the naive loop. Its MSE is also competitive, demonstrating that the ring-buffer architecture does not sacrifice accuracy for speed.

2. Leakage test (look-ahead bias)

A causal filter guarantees that the output at time \(t\) depends only on data up to and including time \(t\). In offline processing, the transform operates on the entire signal at once, so a future event (such as a sudden step) can alter the denoised output at earlier time indices through the wavelet coefficients that span the event boundary. This is called data leakage, or look-ahead bias.

Methodology

To quantify leakage, we use a counterfactual test. We generate two signals that share identical noise for \(t < 128\) but differ after:

Signal A: noise only (no event)
Signal B: same noise, but with a step of amplitude 5 added at \(t \geq 128\)

We run each filter on both signals and measure the total squared difference in their output before the step (\(t < 128\)):

\[\text{Leakage} = \sum_{t=1}^{127} \bigl[\hat{f}_B(t) - \hat{f}_A(t)\bigr]^2\]

A truly causal filter produces identical output for both signals in \([1, 127]\), so its leakage is exactly zero. An offline filter may produce different output because the step’s wavelet coefficients, which have support spanning the boundary, influence the reconstruction at earlier indices.

To make this effect visible, we use longer-support wavelets (CDF 9/7 for rLifting, Daubechies-8 for wavethresh) with floor(log2(N)) decomposition levels. Longer filters have wider support, so their coefficients near the step boundary reach further into the past.

data("leakage_results", package = "rLifting")

kable(leakage_results,
      col.names = c("Method", "Leakage (SSE)"),
      caption = "Counterfactual leakage: lower is better. Zero means no look-ahead bias.")

Counterfactual leakage: lower is better. Zero means no look-ahead bias.
Method	Leakage (SSE)
rLifting causal (CDF 9/7)	0.00000
rLifting offline (CDF 9/7)	1.69639
wavethresh offline (D8)	26.27047

The causal mode of rLifting achieves exactly zero leakage: its output before \(t = 128\) is identical regardless of whether a step follows or not. The filter simply has not seen the future data.

Both offline methods show non-zero leakage. The wavelet coefficients near the step boundary (\(t \approx 128\)) have support that extends into the past region, so the large discontinuity introduced by the step affects the reconstructed values at earlier indices. This is an inherent property of offline wavelet processing with non-trivial filter support — the longer the filter, the further the leakage reaches.

This property is critical for financial backtesting (where look-ahead bias leads to unrealistically optimistic results) and real-time control systems (where the controller must not react to events that have not yet occurred).