  A crucial challenge to successful flare prediction is forecasting periods
that transition between "flare-quiet" and "flare-active". Building on earlier
studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we
describe methodology, details, and results of flare forecasting comparison
efforts, we focus here on patterns of forecast outcomes (success and failure)
over multi-day periods. A novel analysis is developed to evaluate forecasting
success in the context of catching the first event of flare-active periods, and
conversely, of correctly predicting declining flare activity. We demonstrate
these evaluation methods graphically and quantitatively as they provide both
quick comparative evaluations and options for detailed analysis. For the
testing interval 2016-2017, we determine the relative frequency distribution of
two-day dichotomous forecast outcomes for three different event histories
(i.e., event/event, no-event/event and event/no-event), and use it to highlight
performance differences between forecasting methods. A trend is identified
across all forecasting methods that a high/low forecast probability on day-1
remains high/low on day-2 even though flaring activity is transitioning. For
M-class and larger flares, we find that explicitly including persistence or
prior flare history in computing forecasts helps to improve overall forecast
performance. It is also found that using magnetic/modern data leads to
improvement in catching the first-event/first-no-event transitions. Finally,
15% of major (i.e., M-class or above) flare days over the testing interval were
effectively missed due to a lack of observations from instruments away from the
Earth-Sun line.
