[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125124-en":3,"doc-seo-125124-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125124,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Forecasting insect abundance using time series embedding and machine learning - Research paper","Implementing insect monitoring systems enables timely insect-control interventions, yet choosing the correct intervention moment remains difficult due to real-time on-site monitoring constraints. Forecasting insect abundance offers decision support, especially when incorporating climate covariates collected alongside monitoring. A new framework combines statistical modeling, machine learning, and time series embedding using weekly aphid and climate data from two municipalities in Southern Brazil over eight years. A simulation study with probabilistic autoregressive models evaluates performance via Pearson correlation and RMSE.","Journal Pre-proof  \nForecasting insect abundance using time series embedding and machinelearning  \nGabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy,  \nEduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral  \nPII: S1574-9541(24)00476-X  \nDOI: https://doi.org/10.1016/j.ecoinf.2024.102934  \nReference: ECOINF 102934  \nTo appear in: Ecological Informatics  \nReceived date : 19 February 2024  \nRevised date : 29 November 2024  \nAccepted date : 30 November 2024  \nPlease cite this article as: G.R. Palma, R.F. Mello, W.A.C. Godoy et al., Forecasting insect  \nabundance using time series embedding and machine learning. Ecological Informatics (2024), doi:https://doi.org/10.1016/j.ecoinf.2024.102934 .  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as theaddition of a cover page and metadata, and formatting for readability, but itis not yet the definitiveversion of record. This version will undergo additional copyediting, typesetting and review before itis published in its final form, but we are providing this version to give early visibility of the article.Please note that, during the production process, errors may be discovered which could affect thecontent, and all legal disclaimers that apply to the journal pertain.  \n© 2024 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/) .  \nJournal Pre-proof  \nManuscript File Click here to view linked References   \nForecasting insect abundance using time series embeddingand machine learning  \nGabriel R. Palma1,6,∗ Rodrigo F. Mello2 Wesley A.C. Godoy3Eduardo Engel3 Douglas Lau4 Charles Markham1,5 Rafael A. Moral 1,6  \n1. Hamilton Institute, Maynooth University, Maynooth, Ireland;  \n2. Mercado Livre, Osasco, Brazil  \n3. Department of Entomology and Acarology, University of So Paulo, Piracicaba, Brazil;  \n4. Brazilian Agricultural Research Corporation (Embrapa Trigo), Passo Fundo, Rio Grande doSul, Brazil;  \n5. Department of Computer Science, Maynooth University, Maynooth, Ireland;  \n6. Department of Mathematics and Statistics, Maynooth University, Maynooth, Ireland;  \n∗ Corresponding author; e-mail: gabriel.palma.2022@mumail.ie  \nKeywords: Insect outbreak, Integrated Pest Management, Machine Learning, Forecasting,Causality.  \nManuscript type: Research paper.  \nPrepared using the suggested LATEX template for Am. Nat.  \n# Journal Pre-proof\n\nAbstract  \nImplementing insect monitoring systems provides an excellent opportunity to create accurate in-terventions for insect control. However, selecting the appropriate time for an intervention is stillan open question duetothe inherent difficulty of implementing on-site monitoring in real-time.A possible solution to enhance decision-making is to apply forecasting methods to predict insectabundance. However, another layer of complexity is added when other covariates are consideredin the forecasting, such as climate time series collected along the monitoring system. Multiplecombinations of climate time series and their lags can be used to build a forecasting method.Therefore, we propose a new approach to address this problem by combining statistics, machinelearning, and time series embedding. We used two datasets containing a time series of aphidsand climate data collected weekly in two municipalities in Southern Brazil for eight years. Weconduct a simulation study based on a probabilistic autoregressive model with exogenous timeseries based on Poisson and negative binomial distributions to evaluate the performance of ourapproach. We pre-processed the data using our newly proposed approach and more straightfor-ward approaches commonly used to train learning algorithms. We evaluate the performance ofthe selected algorithms by looking at the Pearson correlation and Root Mean Squared Error ob-tained using one-step-ahead forecasting. Based on Random Forests, Lasso-regularised linear re-gression, and LightGBM regression algorithms","cbCairf5BFPL26SS","https://ap.wps.com/l/cbCairf5BFPL26SS","pdf",7008884,1,39,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"What problem does the paper address in insect monitoring and control?\",\"answer\":\"The paper targets the challenge of selecting the right time for interventions when on-site monitoring is hard to implement in real time, and when forecasting must use additional covariates like climate time series.\"},{\"question\":\"How is the forecasting approach constructed?\",\"answer\":\"It combines statistics, machine learning, and time series embedding, integrating multiple climate time series and their lags together with insect abundance forecasting.\"},{\"question\":\"What data and evaluation method are used to test the approach?\",\"answer\":\"The study uses two datasets with weekly aphid time series and climate data from two Southern Brazil municipalities over eight years, evaluating one-step-ahead forecasts using Pearson correlation and Root Mean Squared Error.\"}]","Forecasting insect abundance using time series embedding and machine learning - 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