## data: Residuals from ARIMA(3,0,2) with non-zero mean ## Q* = 6.7928, df = 4, p-value = 0.1473 ## ## Model df: 6. Total lags used: 10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 ## ## Box-Ljung test ## ## data: resid^2 ## ...
它们似乎也有一定的正态分布 ### Ljung-Box test### data: Residuals from ARIMA(3,0,2) with non-zero mean## Q* = 6.7928, df = 4, p-value = 0.1473### Model df: 6. Total lags used: 10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 ### Box-...
它们似乎也有一定的正态分布 ### Ljung-Box test### data: Residuals from ARIMA(3,0,2) with non-zero mean## Q* = 6.7928, df = 4, p-value = 0.1473### Model df: 6. Total lags used: 10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 ### Box-...
## ## data: Residuals from ARIMA(3,0,2) with non-zero mean ## Q* = 6.7928, df = 4, p-value = 0.1473 ## ## Model df: 6. Total lags used: 10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 ## ## Box-Ljung test ## ## data: resid^2 ...
## data: Residuals from ARIMA(3,0,2) with non-zero mean ## Q* = 6.7928, df = 4, p-value = 0.1473 ## ## Model df: 6. Total lags used: 10 1. 2. 3. 4. 5. 6. 7. 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。
## data: ResidualsfromARIMA(3,0,2)withnon-zero mean ## Q* =6.7928, df =4, p-value =0.1473 ## ## Model df:6.Total lags used:10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 ## ## Box-Ljung test ...
## data:ResidualsfromARIMA(3,0,2)withnon-zero mean ##Q*=6.7928,df=4,p-value=0.1473## ## Model df:6.Total lags used:10 现在我们将通过对我们的ARIMA(3,0,2)模型的平方残差应用Ljung-Box测试来检验ARCH效应。 代码语言:javascript 复制 ...
我们将拟合具有自回归阶数 1,差分 0 度和 MA 阶数为 0 的 Arima 模型。 #Fit an AR1 model using Arima fit <- Arima(x, order = c(1, 0, 0)) summary(fit) # Series: x # ARIMA(1,0,0) with non-zero mean # # Coefficients:
ARIMA(4,0,2) with zero mean Coefficients:ar1 ar2 ar3 ar4 ma1 ma2-0.5505 0.2316 0.0880 -0.4325 -0.1944 -0.5977s.e.0.1657 0.1428 0.1402 0.1270 0.1766 0.1732sigma^2 estimated as 417.6:log likelihood=-347.56AIC=709.13 AICc=710.73 BIC=725.63Training set error measures:ME RMSE MAE MPE MAPE ...
ARIMA(1,0,0) with non-zero mean Call: arima(x = x, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.9421 12260.298 s.e. 0.0474 202.717 > predict(arima(x, order = c(1,0,0)), n.ahead=12) $pred Time Series: Start = 53 End = 64 Frequency = 1 [1] 11757.39 1178...