我目前正在处理VALE3.SA的时间序列https://finance.yahoo.com/quote/VALE3.SA/history?p=VALE3.SA

在R上,来自程序包预测的auto.arima()函数返回ARIMA(2,0,0)。 但是当我使用adf.test()和pp.test()时,它们表示不稳定。 我还尝试了diff(),这使我的序列变成平稳的,但是当我尝试强制auto.arima使用1个微分时,它表明ARIMA(0,1,0)。 我应该使用什么型号? 没有平稳性,ARIMA(2,0,0)甚至有效吗?

auto <- auto.arima(tsvale[,"Close"])  ARIMA(2,0,0) with non-zero mean   Coefficients:          ar1      ar2     mean       1.1156  -0.1518  35.9059 s.e.  0.0739   0.0742   6.0635  sigma^2 estimated as 10.89:  log likelihood=-464.95 AIC=937.91   AICc=938.14   BIC=950.63 ```   > adf.test(tsvale[,"Close"])      Augmented Dickey-Fuller Test  data:  tsvale[, "Close"] Dickey-Fuller = -2.2874, Lag order = 5, p-value = 0.4561 alternative hypothesis: stationary  > pp.test(tsvale[,"Close"])      Phillips-Perron Unit Root Test  data:  tsvale[, "Close"] Dickey-Fuller Z(alpha) = -8.1416, Truncation lag parameter = 4, p-value = 0.649 alternative hypothesis: stationary```    auto.arima(tsvale[,"Close"], d = 1) Series: tsvale[, "Close"]  ARIMA(0,1,0)   sigma^2 estimated as 11.13:  log likelihood=-464.39 AIC=930.78   AICc=930.8   BIC=933.96 ``` 

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