Time Series To Supervised

def series_to_supervised(df, n_in=1, n_out=1, targets=[], dropnan=True):
    """
    Converts a time series Pandas DataFrame into a supervised learning problem
    returns: X(t-n_in+1,...,t), y(t+1,....,t+n_out)
    """
    assert n_in > 0 and n_out > 0
    assert all([t in df.columns for t in targets])
    
    n_vars = len(df.columns)
    
    cols, names = list(), list()
    
    # input sequence (t-n_in+1, ... t-1)
    for i in range(n_in-1, 0, -1):
        cols.append(df.shift(i))
        names += [(df.columns[j]+'(t-%d)' % (i)) for j in range(n_vars)]
        
    # current point in time t
    cols.append(df)
    names += [(df.columns[j]+'(t)') for j in range(n_vars)]
    
    # forecast sequence (t+1, ... t+n_out)
    for i in range(1, n_out+1):
        cols.append(df.shift(-i)[targets])
        names += [(c+'(t+%d)' % (i)) for c in targets]
        
    # put it all together
    agg = pd.concat(cols, axis=1)
    agg.columns = names
    
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
        
    s = len(targets)*n_out
    return (agg.loc[:, agg.columns[:-s]], agg.loc[:,  agg.columns[-s:]])