Linear Regression in python with multiple outputs

Question

I have a time series dataset which represented as following:

x=[ 
       [12.19047619,  18.28571429,   6.0952381 ] ,

       [ 80.98765432,  14.17283951,  11.13580247 ] ,

       [ 50.82644628,  16.26446281,   9.14876033 ] , .... ]


and to predicted -->
 Y  = [13.9,  18,   14.987]

How I can use LASSO and SVR linear regression models in python to predict Y (which represented as a vector as shown in the above example)

Answer 1

Both Lasso and SVM are available in sklearn library. Lasso: sklearn.linear_model.Lasso. SVM: sklearn.svm.SVT

An example from Lasso page:

>>> from sklearn import linear_model
>>> clf = linear_model.Lasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False, positive=False, precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False)
>>> print(clf.coef_)
[0.85 0. ]
>>> print(clf.intercept_)
0.15...

In your case clf.fit looks like this:

clf.fit(X, Y)

X should be the size (nn,n)
Y should be the size nn
Where nn is the number of observations (points) and n is the number of variables. So rows in X are observations and columns are different variables.

If you have more variables than observations then you should read this post about the problems you can have with it and how to solve them.