{"title":"如何确定您的时间序列输入是否适合人工智能应用？评估环境分析中的最低数据要求","authors":"Eduart Murcia Botache, Sandra M. Guzmán","doi":"10.32473/edis-ae594-2024","DOIUrl":null,"url":null,"abstract":"This publication is intended for scientists, technicians, and decision-makers who want to start using machine learning (ML) in their projects. It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2024.","PeriodicalId":11471,"journal":{"name":"EDIS","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses\",\"authors\":\"Eduart Murcia Botache, Sandra M. Guzmán\",\"doi\":\"10.32473/edis-ae594-2024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This publication is intended for scientists, technicians, and decision-makers who want to start using machine learning (ML) in their projects. It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2024.\",\"PeriodicalId\":11471,\"journal\":{\"name\":\"EDIS\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EDIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32473/edis-ae594-2024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EDIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32473/edis-ae594-2024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本出版物面向希望在其项目中开始使用机器学习（ML）的科学家、技术人员和决策者。它概述了在使用时间序列 (TS) 数据作为输入的 ML 应用程序时应考虑的因素。作者：Eduart Murcia 和 Sandra M. Guzmán，由 UF/IFAS 农业与生物工程系于 2024 年 1 月出版。
How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses
This publication is intended for scientists, technicians, and decision-makers who want to start using machine learning (ML) in their projects. It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2024.
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