Bayesian Time Series Modelling with Long-Range Dependence
We present a class of models for trend plus stationary component time series, in which the spectral densities of stationary components are represented via non-parametric smoothness priors combined with long-range dependence components. We discuss model fitting and computational issues underlying Bayesian inference under such models, and provide illustration in studies of a climatological time series. These models are of interest to address the questions of existence and extent of apparent long-range effects in time series arising in specific scientific applications.
Time Series Analysis of Diurnal Cycles in Small-Scale Turbulence
Two new time series techniques are employed to study the diurnal effect between thirty to sixty meters depth in the upper equatorial ocean. A modified spectral approach is proposed to identify the period of an unequally spaced series. Based on the exceedances of mixing activities, a new criterion is introduced to identify local nights and days. These two techniques together provide a simple way to demonstrate the existence of the diurnal cycle in deep stratified layers in the ocean. Instead of entertaining complicated models, the proposed approach suggests a simply way to identify the periodicity of an unequally spaced data set of this nature. Similar diurnal effects are detected in the analyses of a subsequent data set.