Multivariate time series analysis from a Bayesian machine learning perspective
Multivariate time series analysis from a Bayesian machine learning perspective
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DOI：
10.1007/s10472-020-09710-6
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发表时间：
2020-09
影响因子：
1.2
通讯作者：
Jinwen Qiu;S. Jammalamadaka;Ning Ning-Ning
中科院分区：
文献类型：
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作者：
Jinwen Qiu;S. Jammalamadaka;Ning Ning-Ning
在本文中，我们从贝叶斯机器学习的角度，通过提出的多变量贝叶斯时间序列(MBTS)模型来进行多变量时间序列分析。多变量结构和贝叶斯框架允许模型利用目标序列之间的关联结构，选择重要特征，同时训练数据驱动模型。对模拟数据和经验数据的广泛分析表明，MBTS模型能够在90%可信区间内覆盖回归系数的真值，选择最重要的预测因子，并在目标序列的绝对值相关性较高的情况下提高预测精度，在一步预测和十步预测中始终优于单变量贝叶斯结构时间序列(BSTS)模型、自回归综合移动平均回归(ARIMAX)模型和多元ARIMAX模型。
In this paper, we perform multivariate time series analysis from a Bayesian machine learning perspective through the proposed multivariate Bayesian time series (MBTS) model. The multivariate structure and the Bayesian framework allow the model to take advantage of the association structure among target series, select important features, and train the data-driven model at the same time. Extensive analyses on both simulated data and empirical data indicate that the MBTS model is able to, cover the true values of regression coefficients in 90%credible intervals, select the most important predictors, and boost the prediction accuracy with higher correlation in absolute value of the target series, and consistently yield superior performance over the univariate Bayesian structural time series (BSTS) model, the autoregressive integrated moving average with regression (ARIMAX) model, and the multivariate ARIMAX (MARIMAX) model, in one-step-ahead forecast and ten-steps-ahead forecast.