基于单变量和多变量先知时间序列模型的欧洲汽车制造商协会(ACEA)水资源有效性预测

P. Riyantoko, Tresna Maulana Fahrudin, K. M. Hindrayani, A. Muhaimin, Trimono
{"title":"基于单变量和多变量先知时间序列模型的欧洲汽车制造商协会(ACEA)水资源有效性预测","authors":"P. Riyantoko, Tresna Maulana Fahrudin, K. M. Hindrayani, A. Muhaimin, Trimono","doi":"10.33005/ijdasea.v1i2.12","DOIUrl":null,"url":null,"abstract":"Time series is one of method to forecasting the data. The ACEA company has competition with opened the\n data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in\n Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater,\n drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using\n univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library\n which develop by Facebook team. We also use the other approach to making the data clean, or the data ready\n to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine\n lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that\n the data not appearing the problem while we tried to forecast. In the other describe, we already get the\n results using univariate and multivariate Prophet method. The multivariate approach has presented the value\n of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.","PeriodicalId":220622,"journal":{"name":"Internasional Journal of Data Science, Engineering, and Anaylitics","volume":"43 7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA\\n (European Automobile Manufacturers Association)\",\"authors\":\"P. Riyantoko, Tresna Maulana Fahrudin, K. M. Hindrayani, A. Muhaimin, Trimono\",\"doi\":\"10.33005/ijdasea.v1i2.12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Time series is one of method to forecasting the data. The ACEA company has competition with opened the\\n data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in\\n Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater,\\n drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using\\n univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library\\n which develop by Facebook team. We also use the other approach to making the data clean, or the data ready\\n to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine\\n lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that\\n the data not appearing the problem while we tried to forecast. In the other describe, we already get the\\n results using univariate and multivariate Prophet method. The multivariate approach has presented the value\\n of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.\",\"PeriodicalId\":220622,\"journal\":{\"name\":\"Internasional Journal of Data Science, Engineering, and Anaylitics\",\"volume\":\"43 7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internasional Journal of Data Science, Engineering, and Anaylitics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33005/ijdasea.v1i2.12\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internasional Journal of Data Science, Engineering, and Anaylitics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33005/ijdasea.v1i2.12","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

时间序列是预测数据的一种方法。ACEA公司与开放的水可用性数据竞争,并使用这些数据进行预测。该数据集即意大利水资源领域的Aquifers-Petrignano数据集包含降雨量、温度、地下水深度、排水量和河流水文等5个参数。本研究将采用时间序列的单变量方法和多变量方法对地下水资料进行深度预测。先知方法是Facebook团队开发的库之一。我们还使用另一种方法使数据干净,或数据准备好预测。我们使用了处理缺失数据、变换、差分、分解时间序列、确定滞后、平稳方法和增强的Dickey-Fuller (ADF)方法。所有的方法将被用来确保数据不出现问题,而我们试图预测。在另一种描述中,我们已经用单变量和多变量的Prophet方法得到了结果。多变量预测方法的MAE为0.82,RMSE为0.99,优于单变量预测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association)
Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信