An algebraic method for constructing stable and consistent autoregressive filters
This work provides a new parameterization approach for autoregressive models that avoids the need for training data, benefiting applications in filtering and prediction of turbulent signals where data may be scarce.
The paper introduces an algebraic method to construct stable and consistent low-order autoregressive models for filtering and predicting nonlinear turbulent signals, requiring only long-time average statistics rather than training data. The method yields significantly more accurate short-term predictions and comparable filtering skill compared to linear regression-based AR models across various test cases.
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams-Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden-Julian Oscillation, a dominant tropical atmospheric wave pattern.