在无方程预测中考虑观测噪声：隐马尔科夫S图

IF 6.3 2区环境科学与生态学 Q1 ECOLOGY

Methods in Ecology and Evolution Pub Date : 2024-07-01 DOI:10.1111/2041-210X.14337

Dylan Esguerra, Stephan B. Munch

引用次数: 0

摘要

生态系统包含众多物种，它们之间以及它们与环境之间存在相互作用。然而，很少有关于所有相关状态变量的数据，这妨碍了推理和预测。经验动态建模（EDM）是对此类部分观测系统进行预测、推理和控制的重要工具。然而，EDM 通常假设可用的时间序列是无差错观测到的。如果不考虑观测噪声，Lyapunov 指数的估计值就会出现严重偏差，并降低预测精度。为解决这一局限性，我们建议将 EDM 纳入隐马尔可夫框架，并使用基于期望最大化（EM）算法的迭代方案来获得过滤后的状态和参数估计。我们在具有一系列加性噪声水平的几个模拟动态系统以及昆虫种群时间序列上评估了这种方法的性能。在与生态时间序列相关的各种噪声水平下，考虑观测噪声提高了种群预测和李亚普诺夫指数（LE）估计的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Accounting for observation noise in equation-free forecasting: The hidden-Markov S-map

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Accounting for observation noise in equation-free forecasting: The hidden-Markov S-map

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来源期刊

Methods in Ecology and Evolution ECOLOGY-

CiteScore

11.60

自引率

3.00%

发文量

236

审稿时长

4-8 weeks

期刊介绍： A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.