I'd really like to see a data sample as well as a code snippet to reproduce your error. Without that, my suggestion will not address your particular error message. It will, however, let you run a multiple regression analysis on a set of time series stored in a pandas dataframe. Assuming that you're using continuous and not categorical values in your time series, here is how I would do it using pandas and statsmodels:

A dataframe with random values:

# Imports
import pandas as pd
import numpy as np
import itertools
np.random.seed(1)
rows = 12
listVars= ['y','x1', 'x2', 'x3']
rng = pd.date_range('1/1/2017', periods=rows, freq='D')
df_1 = pd.DataFrame(np.random.randint(100,150,size=(rows, len(listVars))), columns=listVars)
df_1 = df_1.set_index(rng)
print(df_1)

The function below will let you specify a source dataframe as well as a dependent variable **y** and a selection of independent variables **x1, x2**. Using statsmodels, some desired results will be stored in a dataframe. There, R2 will be of type numeric, while the regression coefficients and p-values will be lists since the numbers of these estimates will vary with the number of independent variables you wish to include in your analysis.

def LinReg(df, y, x, const):
betas = x.copy()
# Model with out without a constant
if const == True:
x = sm.add_constant(df[x])
model = sm.OLS(df[y], x).fit()
else:
model = sm.OLS(df[y], df[x]).fit()
# Estimates of R2 and p
res1 = {'Y': [y],
'R2': [format(model.rsquared, '.4f')],
'p': [model.pvalues.tolist()],
'start': [df.index[0]],
'stop': [df.index[-1]],
'obs' : [df.shape[0]],
'X': [betas]}
df_res1 = pd.DataFrame(data = res1)
# Regression Coefficients
theParams = model.params[0:]
coefs = theParams.to_frame()
df_coefs = pd.DataFrame(coefs.T)
xNames = list(df_coefs)
xValues = list(df_coefs.loc[0].values)
xValues2 = [ '%.2f' % elem for elem in xValues ]
res2 = {'Independent': [xNames],
'beta': [xValues2]}
df_res2 = pd.DataFrame(data = res2)
# All results
df_res = pd.concat([df_res1, df_res2], axis = 1)
df_res = df_res.T
df_res.columns = ['results']
return(df_res)

Here's a test run:

df_regression = LinReg(df = df, y = 'y', x = ['x1', 'x2'], const = True)
print(df_regression)

Output:

results
R2 0.3650
X [x1, x2]
Y y
obs 12
p [0.7417691742514285, 0.07989515781898897, 0.25...
start 2017-01-01 00:00:00
stop 2017-01-12 00:00:00
Independent [const, x1, x2]
coefficients [16.29, 0.47, 0.37]