auto.arima() equivalent for python

Question

I am trying to predict weekly sales using ARMA ARIMA models. I could not find a function for tuning the order(p,d,q) in statsmodels. Currentl

Answer 1

I wrote these utility functions to directly calculate pdq values get_PDQ_parallel require three inputs data which is series with timestamp(datetime) as index. n_jobs will provide number of parallel processor. output will be dataframe with aic and bic value with order=(P,D,Q) in index p and q range is [0,12] while d is [0,1]

import statsmodels 
from statsmodels import api as sm
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.utils import check_array
from functools import partial
from multiprocessing import Pool
def get_aic_bic(order,series):
    aic=np.nan
    bic=np.nan
    #print(series.shape,order)
    try:
        arima_mod=statsmodels.tsa.arima_model.ARIMA(series,order=order,freq='H').fit(transparams=True,method='css')
        aic=arima_mod.aic
        bic=arima_mod.bic
        print(order,aic,bic)
    except:
        pass
    return aic,bic

def get_PDQ_parallel(data,n_jobs=7):
    p_val=13
    q_val=13
    d_vals=2
    pdq_vals=[ (p,d,q) for p in range(p_val) for d in range(d_vals) for q in range(q_val)]
    get_aic_bic_partial=partial(get_aic_bic,series=data)
    p = Pool(n_jobs)
    res=p.map(get_aic_bic_partial, pdq_vals)  
    p.close()
    return pd.DataFrame(res,index=pdq_vals,columns=['aic','bic']) 

Answer 2

possible solution

df=pd.read_csv("http://vincentarelbundock.github.io/Rdatasets/csv/datasets/AirPassengers.csv")

# Define the p, d and q parameters to take any value between 0 and 2
p = d = q = range(0, 2)
print(p)


import itertools
import warnings

# Generate all different combinations of p, q and q triplets
pdq = list(itertools.product(p, d, q))
print(pdq)

# Generate all different combinations of seasonal p, q and q triplets
seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]

print('Examples of parameter combinations for Seasonal ARIMA...')
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))
Examples of parameter combinations for Seasonal ARIMA...
SARIMAX: (0, 0, 1) x (0, 0, 1, 12)
SARIMAX: (0, 0, 1) x (0, 1, 0, 12)
SARIMAX: (0, 1, 0) x (0, 1, 1, 12)
SARIMAX: (0, 1, 0) x (1, 0, 0, 12)

y=df

#warnings.filterwarnings("ignore") # specify to ignore warning messages

for param in pdq:
    for param_seasonal in seasonal_pdq:
        try:
            mod = sm.tsa.statespace.SARIMAX(y,
                                            order=param,
                                            seasonal_order=param_seasonal,
                                            enforce_stationarity=False,
                                            enforce_invertibility=False)

            results = mod.fit()

            print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))
        except:
            continue
ARIMA(0, 0, 0)x(0, 0, 1, 12)12 - AIC:3618.0303991426763
ARIMA(0, 0, 0)x(0, 1, 1, 12)12 - AIC:2824.7439963684233
ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:2942.2733127230185
ARIMA(0, 0, 0)x(1, 0, 1, 12)12 - AIC:2922.178151133141
ARIMA(0, 0, 0)x(1, 1, 0, 12)12 - AIC:2767.105066400224
ARIMA(0, 0, 0)x(1, 1, 1, 12)12 - AIC:2691.233398643673
ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:3890.816777796087
ARIMA(0, 0, 1)x(0, 0, 1, 12)12 - AIC:3541.1171286722
ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:3028.8377323188824
ARIMA(0, 0, 1)x(0, 1, 1, 12)12 - AIC:2746.77973129136
ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:3583.523640623017
ARIMA(0, 0, 1)x(1, 0, 1, 12)12 - AIC:3531.2937768990187
ARIMA(0, 0, 1)x(1, 1, 0, 12)12 - AIC:2781.198675746594
ARIMA(0, 0, 1)x(1, 1, 1, 12)12 - AIC:2720.7023088205974
ARIMA(0, 1, 0)x(0, 0, 1, 12)12 - AIC:3029.089945668332
ARIMA(0, 1, 0)x(0, 1, 1, 12)12 - AIC:2568.2832251221016
ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:2841.315781459511
ARIMA(0, 1, 0)x(1, 0, 1, 12)12 - AIC:2815.4011044132576
ARIMA(0, 1, 0)x(1, 1, 0, 12)12 - AIC:2588.533386513587
ARIMA(0, 1, 0)x(1, 1, 1, 12)12 - AIC:2569.9453272483315
ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:3327.5177587522303
ARIMA(0, 1, 1)x(0, 0, 1, 12)12 - AIC:2984.716706112334
ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:2789.128542154043
ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:2537.0293659293943
ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:2984.4555708516436
ARIMA(0, 1, 1)x(1, 0, 1, 12)12 - AIC:2939.460958374472
ARIMA(0, 1, 1)x(1, 1, 0, 12)12 - AIC:2578.7862352774437
ARIMA(0, 1, 1)x(1, 1, 1, 12)12 - AIC:2537.771484229265
ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:3391.5248913820797
ARIMA(1, 0, 0)x(0, 0, 1, 12)12 - AIC:3038.142074281268
C:\Users\Dell\Anaconda3\lib\site-packages\statsmodels\base\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  "Check mle_retvals", ConvergenceWarning)
ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:2839.809192263449
ARIMA(1, 0, 0)x(0, 1, 1, 12)12 - AIC:2588.50367175184
ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:2993.4630440139595
ARIMA(1, 0, 0)x(1, 0, 1, 12)12 - AIC:2995.049216326931
ARIMA(1, 0, 0)x(1, 1, 0, 12)12 - AIC:2588.2463284315304
ARIMA(1, 0, 0)x(1, 1, 1, 12)12 - AIC:2592.80110502723
ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:3352.0350133621478
ARIMA(1, 0, 1)x(0, 0, 1, 12)12 - AIC:3006.5493366627807
ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:2810.6423724894516
ARIMA(1, 0, 1)x(0, 1, 1, 12)12 - AIC:2559.584031948852
ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:2981.2250436794675
ARIMA(1, 0, 1)x(1, 0, 1, 12)12 - AIC:2959.3142304724834
ARIMA(1, 0, 1)x(1, 1, 0, 12)12 - AIC:2579.8245645892207
ARIMA(1, 0, 1)x(1, 1, 1, 12)12 - AIC:2563.13922589258
ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:3354.7462930846423
ARIMA(1, 1, 0)x(0, 0, 1, 12)12 - AIC:3006.702997636003
ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:2809.3844175191666
ARIMA(1, 1, 0)x(0, 1, 1, 12)12 - AIC:2558.484602766447
ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:2959.885810636943
ARIMA(1, 1, 0)x(1, 0, 1, 12)12 - AIC:2960.712709764296
ARIMA(1, 1, 0)x(1, 1, 0, 12)12 - AIC:2557.945907092698
ARIMA(1, 1, 0)x(1, 1, 1, 12)12 - AIC:2559.274166458508
ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:3326.3285511700374
ARIMA(1, 1, 1)x(0, 0, 1, 12)12 - AIC:2985.868532151721
ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:2790.7677149967103
ARIMA(1, 1, 1)x(0, 1, 1, 12)12 - AIC:2538.820635541546
ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:2963.2789505804294
ARIMA(1, 1, 1)x(1, 0, 1, 12)12 - AIC:2941.2436984747465
ARIMA(1, 1, 1)x(1, 1, 0, 12)12 - AIC:2559.8258191422606
ARIMA(1, 1, 1)x(1, 1, 1, 12)12 - AIC:2539.712354465328

from https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-arima-in-python-3

also see https://github.com/decisionstats/pythonfordatascience/blob/master/time%2Bseries%20(1).ipynb

Answer 3

In conda, use conda install -c saravji pmdarima to install.

The user saravji has put it in anaconda cloud.

then to use,

from pmdarima.arima import auto_arima

(Note that the name pyramid-arima is changed to pmdarima).

Answer 4

As of now, we can directly use pyramid-arima package from pypi

Check https://pypi.org/project/pyramid-arima/

Answer 5

There is now a proper python package to do auto-arima. https://github.com/tgsmith61591/pmdarima

Docs: http://alkaline-ml.com/pmdarima

Example usage: https://github.com/tgsmith61591/pmdarima/blob/master/examples/quick_start_example.ipynb

Answer 6

You can implement a number of approaches:

1. ARIMAResults include aic and bic. By their definition, (see here and here), these criteria penalize for the number of parameters in the model. So you may use these numbers to compare the models. Also scipy has optimize.brute which does grid search on the specified parameters space. So a workflow like this should work:

   def objfunc(order, exog, endog):
       from statsmodels.tsa.arima_model import ARIMA
       fit = ARIMA(endog, order, exog).fit()
       return fit.aic()
   
   from scipy.optimize import brute
   grid = (slice(1, 3, 1), slice(1, 3, 1), slice(1, 3, 1))
   brute(objfunc, grid, args=(exog, endog), finish=None)
   

   Make sure you call brute with finish=None.

2. You may obtain pvalues from ARIMAResults. So a sort of step-forward algorithm is easy to implement where the degree of the model is increased across the dimension which obtains lowest p-value for the added parameter.

3. Use ARIMAResults.predict to cross-validate alternative models. The best approach would be to keep the tail of the time series (say most recent 5% of data) out of sample, and use these points to obtain the test error of the fitted models.