I am on chapter 5.4 residual diagnostics.

In the chapter, there is a test for autocorrelation, with an example for a ljung_box test (below), the p value is 0.637 and it is concluded that the residuals are not distinguishable from a white noise series. My query is that if it was the other way round and if the p value was low eg <0.05, does this mean that any forecasting that you do would be inaccurate?

```
aug %>% features(.innov, ljung_box, lag = 10, dof = 0)
#> # A tibble: 1 x 4
#> Symbol .model lb_stat lb_pvalue
#> <chr> <chr> <dbl> <dbl>
#> 1 GOOG NAIVE(Close) 7.91 0.637
```

^{Referred here by Forecasting: Principles and Practice, by Rob J Hyndman and George Athanasopoulos}