Abstract

We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.

中文翻译：

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对时间趋势的估计和分类，以支持生态系统综合评估

摘要

我们提出一种趋势估计和分类（TREC）方法，以估计多元时间序列观测值之间的主要共同趋势。我们的方法基于两种统计程序，包括趋势建模和判别分析，用于对相似趋势（常见趋势）类别进行分类。我们使用模拟来评估所提出的方法，并将其与时域中的相关动态因素分析进行​​比较，该方法最近被提出来估计渔业时间序列的共同趋势。我们将TREC方法应用于由ICES挪威海和巴伦支海综合评估工作组调查的多元短时间序列数据集。所提出的方法对于短时间序列的应用是鲁棒的，并且可以直接识别和分类观测的主要趋势。