如何加载时间序列，例如：
进入神经网络（LSTM）。到目前为止，我已经看到了这个矩阵被转置的方法（https://github.com/curiousily/Getting-Things-Done-with-Pytorch/blob/master/06.time-series-anomaly-detection-ecg.ipynb )（小时为列，设备为行）。其他人创建自定义数据加载器：http ://www.feeny.org/custom-pytorch-dataset-class-for-timeseries-sequence-windows/并手动创建窗口。
为什么网络没有更多的原生方式来直接处理这个原始输入并学习模式/周期性/异常？如何将这种多设备的多元时间序列本地加载到（pytorch，tensorflow）中，以便生成的 LSTM 正确学习：
单个时间序列的状态（至少在某个窗口内，不一定是整个潜在的无限时间序列）
但在执行预测时还要考虑多个系列/设备/窗口的信息
import pandas as pd from pandas import Timestamp df = pd.DataFrame({'hour': {0: Timestamp('2020-01-01 00:00:00'), 1: Timestamp('2020-01-01 00:00:00'), 2: Timestamp('2020-01-01 00:00:00'), 3: Timestamp('2020-01-01 00:00:00'), 4: Timestamp('2020-01-01 00:00:00'), 5: Timestamp('2020-01-01 01:00:00'), 6: Timestamp('2020-01-01 01:00:00'), 7: Timestamp('2020-01-01 01:00:00'), 8: Timestamp('2020-01-01 01:00:00'), 9: Timestamp('2020-01-01 01:00:00')}, 'metrik_0': {0: 2.020883621337143, 1: 2.808770093182167, 2: 2.5267618429653402, 3: 3.2709845883575346, 4: 3.7984105853602235, 5: 4.0385160093937795, 6: 4.643267594258785, 7: 1.3012379179114388, 8: 3.509304898336378, 9: 2.8664748765561208}, 'metrik_1': {0: 4.580434685779621, 1: 2.933188328317023, 2: 3.999229120882797, 3: 2.9099857745449706, 4: 4.6302055552849, 5: 4.012670194672169, 6: 3.697352153313931, 7: 4.855210603371005, 8: 2.2197913449032254, 9: 2.393605868973481}, 'metrik_2': {0: 3.680527279150989, 1: 2.511065648719921, 2: 3.8350007982479113, 3: 2.4063786290320333, 4: 3.231433617897482, 5: 3.8505378854180115, 6: 5.359150077287063, 7: 2.8966469424805386, 8: 4.554080028058399, 9: 3.3319064764061914}, 'cohort_id': {0: 1, 1: 2, 2: 1, 3: 2, 4: 2, 5: 1, 6: 2, 7: 2, 8: 1, 9: 2}, 'device_id': {0: 1, 1: 3, 2: 4, 3: 2, 4: 5, 5: 4, 6: 3, 7: 2, 8: 1, 9: 5}})