我正在尝试将 AR-EGARCH(1,1) 模型拟合到电价并预测未来一天。我每个小时都在单独建模,所以我只使用时间序列预测 0-1 小时的价格,该时间序列由过去 1000 天的这一小时内的价格组成。为了获得所有 24 小时,我每小时要这样做 23 次。大多数时候预测看起来不错,但有时会导致价格很高,例如 -8000(通常在 40 左右),并且每次执行此操作时,都会显示以下消息:
“arch/univariate/base.py:750: ConvergenceWarning: 优化器返回代码 4。消息是:不等式约束不兼容见 scipy.optimize.fmin_slsqp 代码含义”
出现(不是定期和不同的时间)。我怎样才能解决这个问题?
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,seasonality,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,seasonality,distribution,p,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[-1,0],options={'maxiter':4000},disp='off')
forecast=ar_egarch_fit.forecast().mean.iloc[-1,0]
return(forecast)