论文标题
通用时间序列中独立性的班次测试
A Shift Test for Independence in Generic Time Series
论文作者
论文摘要
我们描述了一个保守的统计测试家庭，以实现两个自相关时间序列的独立性。该系列可能在任何集合中都具有值，其中之一必须是固定的。将两个系列段关联的用户指定函数与通过将固定系列-n到n步骤获得的集合进行比较。如果该系列是独立的，则未缩放值在最高的M移位值中，最多概率为m/（n+1）。对于大N，概率接近m/（2n+1）。如果未降低值在顶部α（n+1）中，则保守测试将拒绝具有显着性α的独立性，并且具有在较大的N极限下有效的近似测试的一半。我们通过测试自相关分类时间序列的测试来说明此框架。
We describe a family of conservative statistical tests for independence of two autocorrelated time series. The series may take values in any sets, and one of them must be stationary. A user-specified function quantifying the association of a segment of the two series is compared to an ensemble obtained by time-shifting the stationary series -N to N steps. If the series are independent, the unshifted value is in the top m shifted values with probability at most m/(N+1). For large N, the probability approaches m/(2N+1). A conservative test rejects independence at significance α if the unshifted value is in the top α(N+1), and has half the power of an approximate test valid in the large N limit. We illustrate this framework with a test for correlation of autocorrelated categorical time series.
