# How can you know that result of auto.arima is accurate?

I’m using auto.arima and forecast package to forecast the COVID-19 dataset. I got my results and graphed them in R. As I’m no statistician, I read many papers available online related to COVID-19 and tried to interpret the graphs and their meaning for my results. For example, p-value, I have greater than 0.05? Some said it’s acceptable, and others reject it. I found some tutorials that say if the p-value is close to 1, it means I have a good forecast model. Some recommend using a Q-Q plot to get an impression of my forecasting distribution. Others recommend I use the histogram of my residuals, and others recommend just to use residuals graphs. Some recommend only using ACF, and (Optionally) include PACF. Some suggest using accuracy in R, which return the measures such as ME, RMSE, MAE, MPE, MAPE, MASE, ACF1. Some recommend only to run summary command and study AIC, AICc, BIC, sigma^2.
I know for an expert; one or two graphs could be enough to conclude the data. I agree the answer depends on my data and results; that’s why I include my results. The question is, what is suitable to judge the forecast performance?
Ljung-Box test data: Residuals from ARIMA(0,2,5) Q* = 6.8118, df = 5, p-value = 0.235 Model df: 5. Total lags used: 10 ME RMSE MAE MPE MAPE MASE ACF1 Training set -2.591304 100.283 60.15786 -0.4603156 3.484356 0.2205352 -0.1870562