Formulate multivariate multistep time series forcasting using traditional machine learning, NOT deep learning 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
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.