非ARIMA模型的可逆性

Question

我正在尝试编写代码来生成一系列arima模型并比较不同的模型.代码如下.

p=0
q=0
d=0
pdq=[]
aic=[]

for p in range(6):
    for d in range(2):
        for q in range(4):
            arima_mod=sm.tsa.ARIMA(df,(p,d,q)).fit(transparams=True)

            x=arima_mod.aic


            x1= p,d,q
            print (x1,x)

            aic.append(x)
            pdq.append(x1)



keys = pdq
values = aic
d = dict(zip(keys, values))
print (d)

minaic=min(d, key=d.get)

for i in range(3):
 p=minaic[0]
    d=minaic[1]
    q=minaic[2]
print (p,d,q)

'df'是时间序列数据.输出如下,

(0, 0, 0) 1712.55522759
(0, 0, 1) 1693.436483044094
(0, 0, 2) 1695.2226857997066
(0, 0, 3) 1690.9437925956158
(0, 1, 0) 1712.74161799
(0, 1, 1) 1693.0408994539348
(0, 1, 2) 1677.2235087182808
(0, 1, 3) 1679.209810237856
(1, 0, 0) 1700.0762847127553
(1, 0, 1) 1695.353190569905
(1, 0, 2) 1694.7907607467605
(1, 0, 3) 1692.235442716487
(1, 1, 0) 1714.5088374907164

ValueError: The computed initial MA coefficients are not invertible
You should induce invertibility, choose a different model order, or you can
pass your own start_params.

即对于订单(1,1,1),该模型是不可逆的.所以过程停在那里.我怎样才能跳过p,d,q的这种不可逆组合,并继续其他组合

Answer 1

使用try: ... except: ...捕获异常并继续

for p in range(6):
    for d in range(2):
        for q in range(4):
            try:
                arima_mod=sm.tsa.ARIMA(df,(p,d,q)).fit(transparams=True)

                x=arima_mod.aic

                x1= p,d,q
                print (x1,x)

                aic.append(x)
                pdq.append(x1)
            except:
                pass
                # ignore the error and go on