i looked in examples on stats models don't see many examples of applying cross-validation time series.
let's have
`in [1]: __future__ import print_function in [2]: import numpy np in [3]: import statsmodels.api sm import pandas pd statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) in [4]: import pandas pd in [5]: statsmodels.tsa.arima_process import arma_generate_sample in [6]: np.random.seed(12345) in [7]: arparams = np.array([.75, -.25]) in [8]: maparams = np.array([.65, .35]) in [9]: in [9]: arparams = np.r_[1, -arparams] in [10]: maparam = np.r_[1, maparams] in [11]: nobs = 250 in [12]: y = arma_generate_sample(arparams, maparams, nobs) in [13]: dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) in [14]: y = pd.timeseries(y, index=dates) /users/pcoyle/anaconda3/bin/ipython:1: futurewarning: timeseries deprecated. please use series #!/bin/bash /users/pcoyle/anaconda3/bin/python.app in [15]: arma_mod = sm.tsa.arma(y, order=(2,2)) in [16]: arma_res = arma_mod.fit(trend='nc', disp=-1) in [17]: print(arma_res.summary()) arma model results ============================================================================== dep. variable: y no. observations: 250 model: arma(2, 2) log likelihood -245.887 method: css-mle s.d. of innovations 0.645 date: mon, 01 aug 2016 aic 501.773 time: 17:51:51 bic 519.381 sample: 01-31-1980 hqic 508.860 - 10-31-2000 ============================================================================== coef std err z p>|z| [95.0% conf. int.] ------------------------------------------------------------------------------ ar.l1.y 0.8411 0.403 2.089 0.038 0.052 1.630 ar.l2.y -0.2693 0.247 -1.092 0.276 -0.753 0.214 ma.l1.y 0.5352 0.412 1.299 0.195 -0.273 1.343 ma.l2.y 0.0157 0.306 0.051 0.959 -0.585 0.616 roots ============================================================================= real imaginary modulus frequency ----------------------------------------------------------------------------- ar.1 1.5618 -1.1289j 1.9271 -0.0996 ar.2 1.5618 +1.1289j 1.9271 0.0996 ma.1 -1.9835 +0.0000j 1.9835 0.5000 ma.2 -32.1794 +0.0000j 32.1794 0.5000 -----------------------------------------------------------------------------` how move example doing cross-validation. ideally cross validation time dependence. suggestions?
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