The notion of long-memory is considered in the case of multivariate time series, not necessarily Gaussian nor stationary. The long-memory characteristics are defined by the long-memory parameters describing the autocorrelation structure of each process and the long-run covariance measuring the coupling between time series. A phase term is present in the model to widen the classes of models. We introduce a representation of the time series by quasi-analytic wavelets for inference in this setting. We first show that the covariance of the wavelet coefficients provides an adequate estimator of the covariance structure of the processes, including the phase term. Consistent estimators are then proposed which is based on a Whittle approximation. Simulations highlight a satisfactory behavior of the estimation on finite samples on some linear time series and on multivariate fractional Brownian motions. An application on a real dataset in neuroscience is displayed, where long-memory and brain connectivity are inferred.
翻译:在多变时间序列中考虑长期记忆的概念, 不一定是高斯或静止的。 长期记忆特性由描述每个过程的自动关系结构和时间序列之间混合的长期共变的长期模拟参数来界定。 模型中存在一个阶段, 以扩大模型的种类。 我们在此设定时采用准分析波子来表示时间序列, 以推断。 我们首先显示波子系数的共变能为包括阶段在内的过程的共变结构提供适当的估量。 然后提出一致的估量, 以惠特尔近似为基础。 模拟突出某些线性时间序列和多变数分数布朗动的定数样本估计的令人满意的行为。 显示神经科学中真实数据集的应用, 在那里可以推断长期和大脑连接。