Goodness of forecast in time series

Question

I used two different methods to forecast a time series data.

The first one used is HoltWinters with Beta and Gamma as FALSE, since I don't see any trend or seasonality in the plot. Below is the result from Box.test

Box.test(fore.holt.stat$residuals, type="Ljung-Box", lag=10)

Box-Ljung test
data:  fore.holt.stat$residuals
X-squared = 10.691, df = 10, p-value = 0.3821

The p-value is 0.3821

I used auto.arima on the data and below is the result

Box.test(fore.arima$residuals, type="Ljung-Box", lag=10)

Box-Ljung test
data:  fore.arima$residuals
X-squared = 14.724, df = 10, p-value = 0.1425

The p-value is 0.14

Question 1:

Can I say that the first model is better since I have a higher p-value?

Below are few other observations :

Model 1:

accuracy(fore.holt.stat)
           ME     RMSE      MAE       MPE     MAPE     MASE       ACF1
Training set 424.9864 10275.55 7930.602 0.8782302 9.251837 0.766108 0.02142331

Model 2:

 accuracy(fore.arima)
           ME     RMSE     MAE        MPE     MAPE      MASE      ACF1
 Training set 284.5242 7243.413 5371.42 -0.1874984 6.036736 0.5183941 0.0100049


Question 2:

Which of the model is correct based on the accuracy() function output?

In both the models, the p-value is high, but the mean of errors is not close to zero.