[英]Supervised learning for time series data
I have following time series data.I want to use classification model.for independent variable i want to pass an array of previous 5 values of feature 1 /feature 2 given some weight.for example on 06-03-2015 for id 1: [ a1 a2 a3 a4 a5] [0.053 0.036 0.044 0.087 0.02 ]
我有以下时间序列数据。我想使用分类模型。对于自变量,我想传递特征 1/特征 2 的前 5 个值的数组,给定一些权重。例如在 2015 年 6 月 3 日,id 1:
[ a1 a2 a3 a4 a5] [0.053 0.036 0.044 0.087 0.02 ]
ID feature1 Date feature2
1 0.053 02-03-2015 0.0115
1 0.05 08-03-2015 0.0117
1 0.099 09-03-2015 0.00355
1 0.006 10-03-2015 0.0088
1 0.007 11-03-2015 0.0968
1 0.0045 12-03-2015 0.08325
1 0.068 13-03-2015 0.0055
1 0.097 14-03-2015 0.0668
1 0.082 18-03-2015 0.0635
2 0.053 21-03-2015 0.0115
2 0.05 26-03-2015 0.0117
2 0.099 27-03-2015 0.00355
2 0.006 28-03-2015 0.0088
2 0.007 29-03-2015 0.0968
2 0.068 31-03-2015 0.0055
2 0.097 01-04-2015 0.0668
2 0.017 02-04-2015 0.0145
2 0.049 06-04-2015 0.0556
How would I assign weights to values on rolling basis where window =5
.weights can between 0 to 1 .so I can multiply them with values and result should go as 1 of the independent variable.How can i use LSTM model for this kind of data.我如何将权重分配给滚动基础上的值,其中
window =5
.weights 可以在 0 到 1 之间。所以我可以将它们与值相乘,结果应该作为自变量的 1。我如何使用 LSTM 模型进行这种数据。
这篇关于 Machine Learning Mastery 的文章将带您了解如何做到这一点。
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