prediction - How to predict with ARIMA in Python -


the following code adapted a related question, have never got working.

def objfunc(order, endog, exog=none):   statsmodels.tsa.arima_model import arima   fit = arima(endog, order, exog, freq='d').fit()   return fit.aic  scipy.optimize import brute grid = (slice(1, 3, 1), slice(1, 3, 1), slice(1, 3, 1)) brute(objfunc, grid, args=([1.350320637, 1.39735651, 1.712129712, 1.718507051, 1.772633255, 1.766728163, 1.590842962, 1.386521041, 1.71810019, 1.743380606, 1.718501449, 1.77709043, 1.823061287, 1.562814653],), finish=none)

it throws exception when bruteforcing:

the computed initial ar coefficients not stationary should induce stationarity, choose different model order, or can pass own start_params.

i read post on problem here, yet didn't find quite helpful.

speaking of seasonality, suggests using x13as handle automatically. instruction on that?

anyway, desperately need rock-solid example on arima in python. there shouldn't pain, isn't it?


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