Online estimation methods for irregular autoregressive models
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AI 根据所提供的论文材料归类：摘要明确研究不规则观测自回归时间序列及其在线参数估计，直接对应时间序列方法。
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不规则自回归模型的在线估计方法
- 研究问题
- 当时间数据持续到达且观测间隔不规则时，如何避免反复使用完整历史数据，在节省计算的同时更新自回归模型参数？
- 方法与思路
- 作者将梯度下降、牛顿步与卡尔曼滤波递推用于不规则观测自回归模型，并以蒙特卡洛实验评估参数估计精度和计算表现。
- 材料陈述的贡献
- 摘要称，三类在线方法对规则及不规则序列均能较准确估计参数、节省计算时间，并比批量估计更快适应序列行为变化。
- 阅读关注点
- 应核对各递推公式、初始化与步长选择、不规则间隔的处理，以及参数变化实验如何设定和精度与耗时如何比较。
- 阅读边界
- 依据的是 arXiv 预印本摘要，不能确认同行评审或期刊接收；摘要未提供误差和耗时数值，也未说明在线估计的收敛保证。
原文摘要
In the last decades, due to the huge technological growth observed, it has become increasingly common that a collection of temporal data rapidly accumulates in vast amounts. This provides an opportunity for extracting valuable information through the estimation of increasingly precise models. But at the same time it imposes the challenge of continuously updating the models as new data become available. Currently available methods for addressing this problem, the so-called online learning methods, use current parameter estimations and novel data to update the estimators. These approaches avoid using the full raw data and speeding up the computations. In this work we consider three online learning algorithms for parameters estimation in the context of time series models. In particular, the methods implemented are: gradient descent, Newton-step and Kalman filter recursions. These algorithms are applied to the recently developed irregularly observed autoregressive (iAR) model. The estimation accuracy of the proposed methods is assessed by means of Monte Carlo experiments. The results obtained show that the proposed online estimation methods allow for a precise estimation of the parameters that generate the data both for the regularly and irregularly observed time series. These online approaches are numerically efficient, allowing substantial computational time savings. Moreover, we show that the proposed methods are able to adapt the parameter estimates quickly when the time series behavior changes, unlike batch estimation methods.