顾嘉亮 / LSTM 时间序列分析预测
https://gitee.com/zhuqufeixiang/LSTM-Time-series-analysis?_from=gitee_search
yangwohenmai/DeepLearningForTSF
https://github.com/yangwohenmai/DeepLearningForTSF
用LSTM进行时间序列预测（单步，多步，单变量，多变量）
https://blog.csdn.net/comli_cn/article/details/106209230
LSTM进阶：使用LSTM进行多维多步的时间序列预测【精品】
https://blog.csdn.net/qq_35649669/article/details/89575949?spm=1001.2101.3001.6650.3&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ELandingCtr%7EHighlightScore-3.queryctrv2&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ELandingCtr%7EHighlightScore-3.queryctrv2&utm_relevant_index=6
时间序列预测结果评价指标
https://zhuanlan.zhihu.com/p/340745560
评价时间序列预测的效果（或精度）的标准（或数学参数）有哪些？
https://www.zhihu.com/question/315976304/answer/738305582
关于深度学习的滞后性问题
https://blog.csdn.net/u011698800/article/details/107607277
keras中 LSTM 的 [samples, time_steps, features] 最终解释
https://blog.csdn.net/hellocsz/article/details/89072016
基于LSTM的时间序列数据(多步)预测
https://www.jianshu.com/p/3b60cefa3109
多变量时间序列的多步预测——LSTM模型
https://zhuanlan.zhihu.com/p/191211602?ivk_sa=1024320u
Python中利用LSTM模型进行时间序列预测分析
https://www.cnblogs.com/arkenstone/p/5794063.html
