Voting classifier using grid search for Time Series
In a time series problem, your input (x) and output (y) are, in the most basic case, the same variable, recorded at different points in time.

If you have T time points: 1, 2, 3, ... T then we think of x as an array of data points, with an index t to access each time point.

Typically y will simply be your x array shifted forward in time (in the below example, by 1 time unit, so $y_t = x_{t-1}$ in vector notation or y[t] == x[t-1] in array notation):

  x  |   y
-----|-----
 0.1 |  NaN
 0.2 |  0.1
 0.3 |  0.2
 0.3 |  0.3
 0.4 |  0.3
 0.3 |  0.4
 0.5 |  0.3
 1.0 |  0.5


Pandas has a shift() method for a time series, which allows you to shift your x column at different time intervals, and create a new y column using that shifted series. See https://stackoverflow.com/questions/10982089/how-to-shift-a-column-in-pandas-dataframe

You can add levels of complexity to this by including multiple time lags, as well as other variables, but this explains the basic principle of how to convert time series forecasting into a supervised learning problem.