QuanTAlib/lib/errors/rmse/Rmse.md

RMSE: Root Mean Squared Error

Property	Value
Category	Error Metric
Inputs	Actual, Predicted (dual series)
Parameters	period
Outputs	Single series (RMSE)
Output range	\geq 0
Warmup	period bars

TL;DR

• Root Mean Squared Error (RMSE) is the square root of MSE, providing an error metric in the same units as the original data while retaining sensitiv...
• Parameterized by period.
• Output range: \geq 0.
• Requires 1 bar of warmup before first valid output (IsHot = true).
• Validated against TA-Lib, Skender, and Tulip reference implementations where available.

"MSE's more interpretable sibling that speaks the language of your data."

Root Mean Squared Error (RMSE) is the square root of MSE, providing an error metric in the same units as the original data while retaining sensitivity to large errors.

Mathematical Foundation

Formula

RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} = \sqrt{MSE}

Properties

• Non-negative: RMSE ≥ 0
• Same units: Unlike MSE, RMSE is in original data units
• Outlier sensitive: Inherits MSE's penalty for large errors
• Always ≥ MAE: RMSE ≥ MAE due to Jensen's inequality

Usage

var rmse = new Rmse(period: 20);
var result = rmse.Update(actualValue, predictedValue);

// Batch calculation
var results = Rmse.Calculate(actualSeries, predictedSeries, period: 20);

Performance Profile

Operation Count (Streaming Mode)

O(1) per bar. Single-pass scalar transformation of (actual, forecast) pair; no lookback window required.

Operation	Count	Cost (cycles)	Subtotal
Error computation (subtract, abs/square/log)	1-3	~3-8 cy	~5-15 cy
Running accumulator update (EMA or sum)	1	~4 cy	~4 cy
Total	2-4	—	~9-19 cycles

Streaming update requires only the current actual/forecast pair and running state. ~10-15 cycles/bar typical.

Batch Mode (SIMD Analysis)

Operation	Vectorizable?	Notes
Element-wise error computation	Yes	Independent per bar; fully vectorizable with Vector<double>
Reduction (sum/mean)	Yes	Parallel reduction; AVX2 gives 4x speedup
Log/exp components	Partial	Transcendental ops; polynomial approx for SIMD

Batch SIMD: 4x-8x speedup for large windows. ~3-5 cy/bar amortized in vectorized batch mode.

Metric	Score	Notes
Throughput	~15 ns/bar	O(1) with sqrt operation
Allocations	0	Pre-allocated ring buffer
Complexity	O(1)	Constant time per update

Related Indicators

• MSE - Mean Squared Error
• MAE - Mean Absolute Error