下面就可以使用ARMA模型进行数据拟合了。这里我不使用ARIMA(ts_diff_1, order=(1, 1, 1))进行拟合,是因为含有差分操作时,预测结果还原老出问题,至今还没弄明白。 view codefrom statsmodels.tsa.arima_modelimportARMA model = ARMA(ts_diff_2, order=(1, 1)) result_arma = model.fit( disp=-1, metho...
例如,我们在上面讨论过,ARIMA(0,1,1)模型似乎是英格兰国王死亡年龄的合理模型。您可以使用R中“arima()”函数的“order”参数在ARIMA模型中指定p,d和q的值。使ARIMA(p,d,q)模型适合此时间序列(我们存储在变量“kingstimeseries”中,见上文),我们输入: > kingstimeseriesarima <- arima(kingstimeseries, order...
# Arima from statsmodels.tsa.arima_model import ARIMA sales_model = ARIMA(sale_data_train['Sales'], order=(1,1,1)) #order中分别为AR\差分和MA的阶数 sales_arima = sales_model.fit(disp=-1, method='css') predict_ts,stderr,conf = sales_arima.forecast(steps=6) #向后预测6期 predic_df...
三、模型拟合 > pre<-arima(wineind,order=c(0,1,2),seasonal=list(order=c(0,1,1),period=12),method="ML") > pre Call: arima(x = wineind, order = c(0, 1, 2), seasonal = list(order = c(0, 1, 1), period = 12), method = "ML") Coefficients: ma1 ma2 sma1 -1.0398 0.1...
order selectionA method of estimating the degree of differencing of an ARIMA process is proposed. This is based on fitting an AR model to the original and to each differenced series and calculating the residual sum of squares. As an application, we suggest an identification method of an ARI ...
(history, order=(1, 1, 1)) # 不要使用差分后的数据, 这里面填写的是步长为d的差分 order对应的参数(p,d, q)14model_fit = model.fit(disp=0)15output =model_fit.forcast()16pred_value =dragon.exp(output[0])17original_value =dragon.exp(test_arima[0])1819predictions.append(pred_value)20...
arima(dy,order=c(p,0,q) ) which.min(aiclist$AIC) 尝试不同的p和q的值,得出最优AIC的模型。 从AIC的结果来看,arima(2,1,1)模型拥有最小的AIC值,因此为最优模型,因此将arima(2,1,1)模型作为最优模型。 对残差序列进行白噪声检验,通常考虑残差序列的随机性,即用伯克斯.皮尔斯 提出的I统计量进行检...
# evaluate an ARIMA model for a given order (p,d,q)defevaluate_arima_model(X,arima_order):# prepare training datasettrain_size=int(len(X)*0.66)train,test=X[0:train_size],X[train_size:]history=[xforxintrain]# make predictionspredictions=list()fortinrange(len(test)):model=ARIMA(history...
seasonal_order=param_seasonal, enforce_stationarity=False, enforce_invertibility=False) 上面的代码产生以下结果 Output SARIMAX(0, 0, 0)x(0, 0, 1, 12) - AIC:6787.3436240402125 SARIMAX(0, 0, 0)x(0, 1, 1, 12) - AIC:1596.711172764114 ...
arima(dy,order=c(p,0,q) ) which.min(aiclist$AIC) 尝试不同的p和q的值,得出最优AIC的模型。 从AIC的结果来看,arima(2,1,1)模型拥有最小的AIC值,因此为最优模型,因此将arima(2,1,1)模型作为最优模型。 对残差序列进行白噪声检验,通常考虑残差序列的随机性,即用伯克斯.皮尔斯 提出的I统计量进行检...