Capinha, CésarCeia-Hasse, AnaKramer, Andrew M.Meijer, Christiaan2025-03-182025-03-182021-031574-9541PURE: 33835746PURE UUID: 05acb9de-1759-4d4c-9d24-7a7754382261Scopus: 85101153503WOS: 000632605900006http://hdl.handle.net/10362/180861Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Funda??o para a Ci?ncia e a Tecnologia [CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020; ACH: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013]. Funding Information: We thank two reviewers who helped improve this work. CC and ACH were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia [CC: CEECIND/02037/2017 , UIDB/00295/2020 and UIDP/00295/2020 ; ACH: PTDC/SAU-PUB/30089/2017 and GHTM- UID/Multi/04413/2013 ]. Publisher Copyright: © 2021 The AuthorsTemporal 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.91659875engDeep learningEcological predictionScalabilitySequential dataTemporal ecologyTime seriesEcology, Evolution, Behavior and SystematicsEcologyModelling and SimulationEcological ModellingComputer Science ApplicationsComputational Theory and MathematicsApplied Mathematicsjournal article10.1016/j.ecoinf.2021.101252https://www.scopus.com/pages/publications/85101153503