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arXiv 2008.10666cond-mat.stat-mechecon.EMphysics.data-an
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动态伊辛模型与离散自回归过程的等价性

On the equivalence between the Kinetic Ising Model and discrete autoregressive processes

Carlo Campajola, Fabrizio Lillo, Piero Mazzarisi, Daniele Tantari

摘要:

二进制随机变量是用于描述各种系统的构建块,从磁自旋到金融时间序列和神经元活动。在统计物理学中,动力学伊辛模型被引入来描述自旋晶格的磁矩的动力学,而在时间序列分析中,离散自回归过程被设计来捕获跨二进制时间序列的多变量依赖结构。在这篇文章中,我们提供了一个严格的证明,这两个模型之间的等价性在一个唯一的和可逆的映射的范围内明确地连接一个模型参数集到其他。我们的结果发现进一步的理由承认,这两个模型提供最大熵分布的二进制时间序列与给定的手段,自相关性,和滞后互相关的顺序一。我们进一步表明,这两个模型之间的等价性允许利用最初开发的一个模型的推理方法在其他的推理。

英文摘要:

Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity. In Statistical Physics the Kinetic Ising Model has been introduced to describe the dynamics of the magnetic moments of a spin lattice, while in time series analysis discrete autoregressive processes have been designed to capture the multivariate dependence structure across binary time series. In this article we provide a rigorous proof of the equivalence between the two models in the range of a unique and invertible map unambiguously linking one model parameters set to the other. Our result finds further justification acknowledging that both models provide maximum entropy distributions of binary time series with given means, auto-correlations, and lagged cross-correlations of order one. We further show that the equivalence between the two models permits to exploit the inference methods originally developed for one model in the inference of the other.

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