2 research outputs found
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Fitting autoregressive moving average (ARMA) time series models requires
model identification before parameter estimation. Model identification involves
determining the order of the autoregressive and moving average components which
is generally performed by inspection of the autocorrelation and partial
autocorrelation functions or other offline methods. In this work, we regularize
the parameter estimation optimization problem with a nonsmooth hierarchical
sparsity-inducing penalty based on two path graphs that allows performing model
identification and parameter estimation simultaneously. A proximal block
coordinate descent algorithm is then proposed to solve the underlying
optimization problem efficiently. The resulting model satisfies the required
stationarity and invertibility conditions for ARMA models. Numerical studies
supporting the performance of the proposed method and comparing it with other
schemes are presented
Nonconvexity of the Stability Domain of Digital Filters
191 M. G. Amin, “On the application of the singlc-pole filter in recursiv