Trend locally stationary wavelet processes
提出一种能同时处理一阶和二阶非平稳性的时间序列建模框架，通过引入趋势成分扩展局部平稳小波模型，并给出估计理论，应用于全球平均海温序列分析。
Most time series observed in practice exhibit first‐ as well as second‐order non‐stationarity. In this article we propose a novel framework for modelling series with simultaneous time‐varying first‐ and second‐order structure, removing the restrictive zero‐mean assumption of locally stationary wavelet processes and extending the applicability of the locally stationary wavelet model to include trend components. We develop an associated estimation theory for both first‐ and second‐order time series quantities and show that our estimators achieve good properties in isolation of each other by making appropriate assumptions on the series trend. We demonstrate the utility of the method by analysing the global mean sea temperature time series, highlighting the impact of the changing climate.