无需模型识别的 ARMA 时间序列模型拟合：一种近端方法
Fitting ARMA Time Series Models without Identification: A Proximal Approach
AI总结：
针对 ARMA 模型需先识别阶数再估计参数的问题，提出基于双路径图分层稀疏惩罚的联合优化方法，并用近端块坐标下降高效求解，保证平稳性与可逆性。
摘要：
拟合自回归滑动平均（ARMA）时间序列模型需要在参数估计之前进行模型识别。模型识别涉及确定自回归和滑动平均成分的阶数，通常通过检查自相关函数和偏自相关函数，或其他离线方法来完成。在本研究中，我们采用一种基于两个路径图的非光滑分层稀疏诱导惩罚，对参数估计优化问题进行正则化，从而能够同时执行模型识别和参数估计。随后，提出了一种近端块坐标下降算法，以高效求解底层优化问题。所得模型满足 ARMA 模型所需的平稳性和可逆性条件。文中还给出了支持所提方法的数值结果。
英文摘要：
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 non-smooth hierarchical sparsity-inducing penalty based on two path graphs that allow 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 results supporting the proposed method are also presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
