Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting
Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting
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DOI：
10.1609/aaai.v35i8.16907
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发表时间：
2020-09
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影响因子：
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通讯作者：
Amirreza Farnoosh;Bahar Azari;S. Ostadabbas
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文献类型：
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作者：
Amirreza Farnoosh;Bahar Azari;S. Ostadabbas
我们引入深度切换自回归分解（DSARF），这是一种时空数据的深度生成模型，能够揭示数据中的重复模式并执行稳健的短期和长期预测。与其他因子分析方法类似，DSARF 通过时间相关权重和空间相关因子之间的乘积来近似高维数据。这些权重和因子又以使用随机变分推理推断出的低维潜在变量表示。 DSARF 与最先进的技术不同，它根据马尔可夫先验控制的深度切换向量自回归似然对权重进行参数化，能够捕获权重之间的非线性相互依赖性来表征多模态时间动态。这产生了灵活的分层深度生成因子分析模型，该模型可以扩展到（i）提供从过程动态中抽象的潜在可解释状态的集合，以及（ii）在复杂的多关系设置中执行短期和长期向量时间序列预测。我们进行了大量的实验，其中包括来自气候变化、天气预报、交通、传染病传播和非线性物理系统等广泛应用的模拟数据和真实数据，证明了与最先进的方法相比，DSARF 在长期和短期预测误差方面具有优越的性能。
We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latent variables that are inferred using stochastic variational inference. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive likelihood governed with a Markovian prior, which is able to capture the non-linear inter-dependencies among weights to characterize multimodal temporal dynamics. This results in a flexible hierarchical deep generative factor analysis model that can be extended to (i) provide a collection of potentially interpretable states abstracted from the process dynamics, and (ii) perform short- and long-term vector time series prediction in a complex multi-relational setting. Our extensive experiments, which include simulated data and real data from a wide range of applications such as climate change, weather forecasting, traffic, infectious disease spread and nonlinear physical systems attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.