Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.

时间数据在生态学中无处不在，生态学家经常面临着将这些数据准确区分为预定类别（例如生物实体或生态状态）的挑战。通常的方法包括将时间序列转换为用户定义的特征，然后将这些特征用作常规统计或机器学习模型中的预测变量。在这里，我们建议使用深度学习模型作为该方法的替代方法。最近的深度学习技术可以直接从时间序列执行分类，从而消除了主观和消耗资源的数据转换步骤，并有可能改善分类结果。我们描述了一些与时间序列分类相关的深度学习架构，并展示了如何测试这些架构及其超参数并将其用于当前的分类问题。我们使用来自不同生态子学科的三个案例研究来说明该方法：i）从脉动谱图中鉴定昆虫种类；ii）根据气候时间序列进行物种分布建模，以及iii）从连续气象数据中对物候期进行分类。深度学习方法提供了对生态敏感且准确的分类，证明了其在生态子领域中广泛应用的潜力。