在 Python 中拟合 AR-EGARCH 模型时“不等式约束不兼容”是什么意思？

如何解决在 Python 中拟合 AR-EGARCH 模型时“不等式约束不兼容”是什么意思？

我正在尝试将 AR-EGARCH(1,1) 模型拟合到电价并预测未来一天。我每小时单独建模，因此我仅使用时间序列预测 0-1 小时的价格，该时间序列由过去 1000 天的这一小时的价格组成。为了获得所有 24 小时，我每小时执行 23 次。大多数时候预测似乎不错，但有时会导致像 -8000（通常约为 40）这样的大价格，并且每次这样做时，消息：

"arch/univariate/base.py:750: ConvergenceWarning: 优化器返回代码 4。消息是： 不等式约束不相容 代码含义见scipy.optimize.fmin_slsqp"

出现（不是定期出现并且在不同时间出现）。我该如何解决这个问题？

def forecast_ar_garch_egarch_hourly(first_forecast_start,seasonality,distribution,length):
   
    df_ar_egarch=pd.DataFrame()
    
    for i in range(0,24): # for each hour
        #get electricity prices for hour i in one dataframe
        electricity_prices_hour_i= pd.DataFrame(electricity_prices[electricity_prices['hour']==i]['Price'],index=electricity_prices[electricity_prices['hour']==i].index)
        #forecast start
        forecast_day=first_forecast_day+d.timedelta(hours=i)
        #how much past data should be included
        start_model=forecast_day-d.timedelta(days=length) 
        end_model=forecast_day-d.timedelta(days=1)
        
        df_hour_i=electricity_prices_hour_i[start_model:end_model]
        
        #use pmdarima.auto_arima to get the optimal parameter combination (but only the AR(P)-part)
        diff=ndiffs(df_hour_i,test='adf')
        ar_fit=pm.auto_arima(df_hour_i,max_p=6,start_q=0,max_q=0,d=diff,m=seasonality,start_Q=0,max_Q=0,max_P=16,test='adf',stepwise=True,trace=True)
        p,diff,q= ar_fit.order
        P,Diff,Q,m= ar_fit.seasonal_order
        
        #get prediction from ar_egarch_pred function
        prediction_egarch=ar_egarch_pred(df_stundei,M,p,P)
        
        df_ar_egarch=df_ar_egarch.append({'Electricity price EGARCH':prediction_egarch},ignore_index=True)
        i+1
    
    df_ar_egarch.index=pd.date_range(start=first_forecast_start,end=first_forecast_start+d.timedelta(hours=23),freq='60min')
    return(df_ar_garch,df_ar_egarch)

def ar_egarch_pred(data,P):
    #distribution : 't','normal' or 'skew'
    #seasonality= frequency of seasonality (=7) since I assume weekly seasonality
    
    #get the AR(p) lags
    lags_ar=[]
    for i in range(1,p+1):
        lags_ar=lags_ar+[i]
    for j in range(0,P):
        lags_ar=lags_ar+[(np.linspace(M,P*M,num=P)[j])]
        
    lags_ar=[int(x) for x in lags_ar]
    
    #fit and forecast with egarch model one step ahead and return this value
    ar_egarch=arch_model(modelldaten,mean='AR',lags=lags_ar,vol='EGARCH',p=1,o=1,q=1,dist=distribution)
    ar_egarch_fit=ar_egarch.fit(first_obs=modelldaten.iloc[0,0],last_obs=modelldaten.iloc[-1,options={'maxiter':4000},disp='off')
    forecast=ar_egarch_fit.forecast().mean.iloc[-1,0]
    return(forecast)