论文标题
拟合ARMA时间序列模型没有识别：近端方法
Fitting ARMA Time Series Models without Identification: A Proximal Approach
论文作者
论文摘要
拟合自回旋运动平均值（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旋和移动平均组件的顺序，这些组件通常通过检查自相关和部分自相关功能或其他离线方法来执行。在这项工作中，我们根据两个路径图，将参数估计优化问题与非平滑层次稀疏性诱导惩罚进行正规化，这些惩罚允许同时执行模型识别和参数估计。然后提出了近端块坐标下降算法，以有效地解决潜在的优化问题。所得模型满足ARMA模型所需的平稳性和可逆条件。还提出了支持该方法的数值结果。
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.
