# Data-Driven Output Prediction and Control of Stochastic Systems: An Innovation-Based Approach

arXiv: https://arxiv.org/abs/2209.08995 · HTML: https://mlsanity.com/paper/2209.08995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction](https://mlsanity.com/paper/2505.05916/) (2025)
- [Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise](https://mlsanity.com/paper/2506.02613/) (2025)

## Abstract (arXiv)

Recent years have witnessed a booming interest in data-driven control of dynamical systems. However, the implicit data-driven output predictors are vulnerable to uncertainty such as process disturbance and measurement noise, causing unreliable predictions and unexpected control actions. In this brief, we put forward a new data-driven approach to output prediction of stochastic linear time-invariant (LTI) systems. By utilizing the innovation form, the uncertainty in stochastic LTI systems is recast as innovations that can be readily estimated from input-output data without knowing system matrices. In this way, by applying the fundamental lemma to the innovation form, we propose a new innovation-based data-driven output predictor (OP) of stochastic LTI systems, which bypasses the need for identifying state-space matrices explicitly and building a state estimator. The boundedness of the second moment of prediction errors in closed-loop is established under mild conditions. The proposed data-driven OP can be integrated into optimal control design for better performance. Numerical simulations demonstrate the outperformance of the proposed innovation-based methods in output prediction and control design over existing formulations.