KalmanRegressionChannel

Summary

KalmanRegressionChannel is RTTA's streaming implementation of: Online Kalman regression with prediction channel and spread.

Update API

result = rtta.KalmanRegressionChannel().update(real0, real1)

The update(...) call consumes one observation using real0, real1. advance(...) uses the same inputs when the caller wants to update state without materializing a Python return value.

Theory Of Operation

KalmanRegressionChannel treats the input stream as noisy observations of a latent state. Each call performs the standard predict/update cycle, then projects the updated state into the public scalar or result fields.

Recurrence

Let \(z_t = (real0_t, real1_t)\) denote the observation consumed by one update(...) call and let \(\theta\) denote constructor parameters such as window lengths, thresholds, and smoothing constants.

\[\hat{x}_{t|t-1}=F\hat{x}_{t-1|t-1}, \qquad P_{t|t-1}=FP_{t-1|t-1}F^\top+Q\]

\[K_t=P_{t|t-1}H^\top(HP_{t|t-1}H^\top+R)^{-1}\]

\[\hat{x}_{t|t}=\hat{x}_{t|t-1}+K_t(z_t-H\hat{x}_{t|t-1}), \qquad P_{t|t}=(I-K_tH)P_{t|t-1}\]

update(...) returns a result struct with fields slope, intercept, middle, upper, lower, spread.

Implementation Notes

The recurrence is implemented in src/rtta/indicator.cpp in class KalmanRegressionChannel.

Reference

Background reference