论文标题
不规则采样时间序列的转换成本频谱
Transformation cost spectrum for irregularly sampled time series
论文作者
论文摘要
在各个学科中，不规则采样的时间序列分析是一个常见的问题。由于常规方法不直接适用于不规则采样的时间序列，因此使用了常见的插值方法。但是，这会导致数据失真，因此会偏向进一步的分析。我们提出了一种方法，该方法会产生定期采样的时间序列成本频谱，而信息损失最少。该频谱中的每个时间序列都是固定序列，并且充当差异过滤器。转换成本方法得出了连续和任意尺寸的细分之间的差异。获得常规采样后，进行复发图分析以区分制度转变。该方法应用于原型模型，以验证其性能和位于非洲周围附近的不同古气候代理数据集，以识别过去500万年内的关键气候过渡期及其特征性能。
Irregularly sampled time series analysis is a common problem in various disciplines. Since conventional methods are not directly applicable to irregularly sampled time series, a common interpolation approach is used; however, this causes data distortion and consequently biases further analyses. We propose a method that yields a regularly sampled time series spectrum of costs with minimum information loss. Each time series in this spectrum is a stationary series and acts as a difference filter. The transformation costs approach derives the differences between consecutive and arbitrarily sized segments. After obtaining regular sampling, recurrence plot analysis is performed to distinguish regime transitions. The approach is applied to a prototypical model to validate its performance and to different palaeoclimate proxy data sets located around Africa to identify critical climate transition periods during the last 5 million years and their characteristic properties.
