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How do you represent multivariate multistep data using traditional machine learning? I know this seems like a tailored problem for RNN/LSTM, but I am wondering what the alternative machine learning solution would be.

Ex.

time, feature_1, feature_2, feature_3
1     25         150        0.7       
2     25.3       147        1.1        
3     25.5       145        0.8
...
10    26.7       165        1.2

Now I want to predict feature_1 at time 10 using the first 3 time steps of all features, and get a dataset like this

feature_1,       feature_2,       feature_3,       target
[25,25.3,25.5]   [150,147,145]    [0.7,1.1,0.8]    26.7
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  • $\begingroup$ Would you mean something like vector autoregression? $\endgroup$
    – Dave
    Jun 10 '20 at 10:08
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You can always reduce the forecasting problem to a tabular regression problem and then apply any tabular regression algorithm you like. Here's a good explanation of how this works.

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