LSTM与Transformer双模型时序预测代码包(含ETT/pollution数据+训练脚本+可视化结果)

上传者: ujm567890 | 上传时间: 2026-05-19 11:37:12 | 文件大小: 28.47MB | 文件类型: ZIP
直接可用的时间序列预测代码集合,内置LSTM和Transformer两种主流模型实现,支持GPU加速训练。包含3个真实时序数据集:ETTm1(电力变压器负荷)、ETTh1(小时级电力负荷)、pollution(多变量空气污染监测数据)。所有Python源码(RNN.py、RNN_gpu.py、Transformer.py)均结构清晰、注释完整,适配PyTorch环境;配套7个XML配置文件用于IDEA项目管理,14张PNG图片涵盖模型结构图、训练损失曲线、预测效果对比图等关键可视化结果;readme.txt提供快速上手说明,state文件保存预训练模型状态便于复现。无需额外数据处理,下载即跑通,适合算法验证、课程实践或工程原型开发。

文件下载

资源详情

[{"title":"( 25 个子文件 28.47MB ) LSTM与Transformer双模型时序预测代码包(含ETT/pollution数据+训练脚本+可视化结果)","children":[{"title":"I7myAMwYG2Aw6KwDBcrk-master-719e906585bb9a43405192bac2e148148388b47b","children":[{"title":"data","children":[{"title":"ETTh1.csv <span style='color:#111;'> 2.47MB </span>","children":null,"spread":false},{"title":"pollution.csv <span style='color:#111;'> 2.20MB </span>","children":null,"spread":false},{"title":"ETTm1.csv <span style='color:#111;'> 9.88MB </span>","children":null,"spread":false}],"spread":true},{"title":"Transformer.py <span style='color:#111;'> 6.76KB </span>","children":null,"spread":false},{"title":"RNN_gpu.py <span style='color:#111;'> 4.92KB </span>","children":null,"spread":false},{"title":"RNN.py <span style='color:#111;'> 4.88KB </span>","children":null,"spread":false},{"title":"README.assets","children":[{"title":"image-20230706131124989.png <span style='color:#111;'> 13.59KB </span>","children":null,"spread":false},{"title":"image-20230704152304997.png <span style='color:#111;'> 267.43KB </span>","children":null,"spread":false},{"title":"image-20230704160535131.png <span style='color:#111;'> 175.52KB </span>","children":null,"spread":false},{"title":"image-20230706131424637.png <span style='color:#111;'> 364.22KB </span>","children":null,"spread":false},{"title":"image-20230706132456426.png <span style='color:#111;'> 166.67KB </span>","children":null,"spread":false},{"title":"image-20230706130608372.png <span style='color:#111;'> 845.72KB </span>","children":null,"spread":false},{"title":"image-20230704171437794.png <span style='color:#111;'> 9.71KB </span>","children":null,"spread":false},{"title":"image-20230706114916264.png <span style='color:#111;'> 133.58KB </span>","children":null,"spread":false},{"title":"image-20230704154845211.png <span style='color:#111;'> 49.30KB </span>","children":null,"spread":false},{"title":"image-20230704152245298.png <span style='color:#111;'> 267.43KB </span>","children":null,"spread":false},{"title":"image-20230704142311220.png <span style='color:#111;'> 133.58KB </span>","children":null,"spread":false},{"title":"image-20230704170336239.png <span style='color:#111;'> 146.03KB </span>","children":null,"spread":false},{"title":"image-20230706130502098.png <span style='color:#111;'> 258.88KB </span>","children":null,"spread":false},{"title":"image-20230704171511751.png <span style='color:#111;'> 19.76KB </span>","children":null,"spread":false}],"spread":false},{"title":"requirements.txt <span style='color:#111;'> 79B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 1.16KB </span>","children":null,"spread":false},{"title":".inscode <span style='color:#111;'> 73B </span>","children":null,"spread":false},{"title":"model state","children":[{"title":"state <span style='color:#111;'> 26.23MB </span>","children":null,"spread":false}],"spread":true},{"title":"readme.txt <span style='color:#111;'> 215B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明