时间序列机器学习:用于时间序列分析的机器学习模型

上传者: 42164702 | 上传时间: 2023-04-21 00:06:30 | 文件大小: 101KB | 文件类型: ZIP
机器学习的时间序列预测 一组预测时间序列的不同机器学习模型,具体来说是给定货币图表和目标的市场价格。 要求 必需的依赖项: numpy 。 其他依赖项是可选的,但是为了使最终模型更多样化,建议安装以下软件包: tensorflow , xgboost 。 经过python版本测试:2.7.14、3.6.0。 取得资料 有一个内置的数据提供程序,可以从获取数据。 目前,所有模型都已通过加密货币图表进行了测试。 提取的数据格式是标准安全性:日期,最高,最低,打开,关闭,交易量,报价量,weightedAverage。 但是模型与特定的时间序列特征无关,并且可以使用这些特征的子集或超集进行训练。 要获取数据, 从根目录运行脚本: # Fetches the default tickers: BTC_ETH, BTC_LTC, BTC_XRP, BTC_ZEC for all time periods. $ ./run_fetch.py 默认情况下,将提取Poloniex中所有可用时间段(天,4h,2h,30m,15m,5m)的数据,并将其存储在_data目录中。 您可以通过命令行参

文件下载

资源详情

[{"title":"( 40 个子文件 101KB ) 时间序列机器学习:用于时间序列分析的机器学习模型","children":[{"title":"time-series-machine-learning-master","children":[{"title":"train","children":[{"title":"job_info.py <span style='color:#111;'> 1.64KB </span>","children":null,"spread":false},{"title":"evaluator.py <span style='color:#111;'> 1.90KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 148B </span>","children":null,"spread":false},{"title":"job_runner.py <span style='color:#111;'> 3.63KB </span>","children":null,"spread":false}],"spread":true},{"title":"models","children":[{"title":"tensorflow_model.py <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false},{"title":"nn_ops.py <span style='color:#111;'> 2.21KB </span>","children":null,"spread":false},{"title":"nn_model.py <span style='color:#111;'> 2.43KB </span>","children":null,"spread":false},{"title":"cnn_model.py <span style='color:#111;'> 2.25KB </span>","children":null,"spread":false},{"title":"model.py <span style='color:#111;'> 560B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 553B </span>","children":null,"spread":false},{"title":"xgboost_model.py <span style='color:#111;'> 782B </span>","children":null,"spread":false},{"title":"linear_model.py <span style='color:#111;'> 772B </span>","children":null,"spread":false},{"title":"rnn_model.py <span style='color:#111;'> 3.03KB </span>","children":null,"spread":false}],"spread":true},{"title":"predict","children":[{"title":"__init__.py <span style='color:#111;'> 189B </span>","children":null,"spread":false},{"title":"ensemble.py <span style='color:#111;'> 3.54KB </span>","children":null,"spread":false},{"title":"model_io.py <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false}],"spread":true},{"title":"run_visual.py <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false},{"title":"run_train.py <span style='color:#111;'> 4.04KB </span>","children":null,"spread":false},{"title":"run_fetch.py <span style='color:#111;'> 354B </span>","children":null,"spread":false},{"title":".idea","children":[{"title":"misc.xml <span style='color:#111;'> 220B </span>","children":null,"spread":false},{"title":"time_series_machine_learning.iml <span style='color:#111;'> 466B </span>","children":null,"spread":false},{"title":"codeStyleSettings.xml <span style='color:#111;'> 365B </span>","children":null,"spread":false},{"title":"inspectionProfiles","children":[{"title":"profiles_settings.xml <span style='color:#111;'> 228B </span>","children":null,"spread":false}],"spread":true},{"title":"modules.xml <span style='color:#111;'> 308B </span>","children":null,"spread":false},{"title":"vcs.xml <span style='color:#111;'> 180B </span>","children":null,"spread":false}],"spread":true},{"title":"util","children":[{"title":"logging.py <span style='color:#111;'> 985B </span>","children":null,"spread":false},{"title":"data_util.py <span style='color:#111;'> 2.83KB </span>","children":null,"spread":false},{"title":"cmdline.py <span style='color:#111;'> 2.02KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 361B </span>","children":null,"spread":false},{"title":"collection_util.py <span style='color:#111;'> 510B </span>","children":null,"spread":false},{"title":"data_set.py <span style='color:#111;'> 1.17KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 11.09KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 7.27KB </span>","children":null,"spread":false},{"title":".images","children":[{"title":"btc_ltc_prediction.png <span style='color:#111;'> 38.00KB </span>","children":null,"spread":false},{"title":"btc_eth_prediction.png <span style='color:#111;'> 32.19KB </span>","children":null,"spread":false}],"spread":true},{"title":"run_predict.py <span style='color:#111;'> 1002B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 100B </span>","children":null,"spread":false},{"title":"poloniex","children":[{"title":"__init__.py <span style='color:#111;'> 225B </span>","children":null,"spread":false},{"title":"fetch_data.py <span style='color:#111;'> 2.14KB </span>","children":null,"spread":false},{"title":"api.py <span style='color:#111;'> 1.42KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"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