Deep learning for time series classification in ecology

O_LITime series classification consists of assigning time series into one of two or more predefined classes. This procedure plays a role in a vast number of ecological classification tasks, including species identification, animal behaviour analysis, predictive mapping, or the detection of critical transitions in ecological systems. In ecology, the usual approach to time series classification consists of transforming the time series into static predictors and then using these in conventional statistical or machine learning models. However, recent deep learning approaches now enable the classification using the raw time series data, avoiding the need for domain expertise, eliminating subjective and resource-consuming data transformation procedures, and potentially improving classification results. C_LIO_LIWe here introduce ecologists to time series classification using deep learning models. 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 problem at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) species identification from wingbeat spectrograms; ii) species distribution modelling from time series of climatic variables and iii) the classification of phenological phases from continuous meteorological data. C_LIO_LIThe deep learning approach delivered ecologically robust and high performing classifications for the three case studies. The results obtained also allowed us to point future research directions and highlight current limitations. C_LIO_LIWe demonstrate the high potential and wide applicability of deep learning for time series classification in ecology. We recommend this approach be considered as an alternative to commonly used techniques requiring the transformation of time series data. C_LI