Sub-sequence incidence analysis within series of Bernoulli trials: application in characterisation of time series dynamics
提出一种新的非参数方法，通过分析序列运动方向模式的发生率来刻画时间序列动态，适用于任何尺度数据，并推导了随机行为零假设下的分布族及其矩生成公式，可用于高频或长期金融与会计数据。
This paper presents a new and widely applicable nonparametric approach to the characterisation of time series dynamics. The approach involves analysis of the incidence of occurrence of patterns in the direction of movement of the series, and may readily be applied to time series data measured on any scale. The paper includes derivations of analytic forms for two (infinite) families of distributions under the null hypothesis of random behaviour, and of a useful analytic form for the generation of the moments of these distributions. The distributions are asymptotically normal, so allowing for straightforward application of the approach presented in the paper too long series of high frequency and/or extended time period data. Areas of application in finance and accounting are suggested.