We propose a new statistical test to identify non-stationary frequency-modulated stochastic processes from time series data. Our method uses the instantaneous phase as a discriminatory statistics with reliable critical values derived from surrogate data. We simulated an oscillatory second-order autoregressive process to evaluate the size and power of the test. We found that the test we propose is able to correctly identify more than 99% of non-stationary data when the frequency of simulated data is doubled after the first half of the time series. Our method is easily interpretable, computationally cheap and does not require choosing hyperparameters that are dependent on the data.
翻译:我们建议进行新的统计测试,从时间序列数据中查明非静止频率调控的随机过程。 我们的方法使用瞬时阶段作为歧视性的统计数据,从代用数据中得出可靠的关键值。 我们模拟了一种随机二级自动递减过程,以评估试验的规模和功率。 我们发现,当模拟数据的频率在时间序列前半部分之后翻倍时,我们提议的测试能够正确识别超过99%的非静止数据。 我们的方法容易解释,计算成本低,不需要选择依赖数据的超参数。