10fold cross-validation on time series

[ulrich48155]

Hello python experts, 
I'm relatively new to python but have to solve a problem for a university project. I hope you guys can help me. 

My task is to do a 10fold cross-validation on a time series in which 90% should be training data and 10% should be for testing. In the end I should evaluate the testing set with the RMSE. Furhtermore, the data should not be shuffeld, as it is a time series. After doing some research I came up with this:

X = mergedf['AnzahlTweets']
y = mergedf['Kurs']

for train_index, test_index in tscv.split(X):
   print("TRAIN:", train_index, "TEST:", test_index)
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]

linreg=LinearRegression()
linreg.fit(X_train,y_train)
y_pred=linreg.predict(X_test)
print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Now I got two questions: is this the right approach for my task and if so, why do I get this error 'ValueError: Input contains NaN, infinity or a value too large for dtype('float64').'? It seems like X_train, X_test, y_train, y_test just consists of 'NaN' although X and y have values.

I hope you can help me. Thanks in advance!