  We examine the novel problem of the estimation of transaction arrival
processes in the intraday electricity markets. We model the inter-arrivals
using multiple time-varying parametric densities based on the generalized F
distribution estimated by maximum likelihood. We analyse both the in-sample
characteristics and the probabilistic forecasting performance. In a rolling
window forecasting study, we simulate many trajectories to evaluate the
forecasts and gain significant insights into the model fit. The prediction
accuracy is evaluated by a functional version of the MAE (mean absolute error),
RMSE (root mean squared error) and CRPS (continuous ranked probability score)
for the simulated count processes. This paper fills the gap in the literature
regarding the intensity estimation of transaction arrivals and is a major
contribution to the topic, yet leaves much of the field for further
development. The study presented in this paper is conducted based on the German
Intraday Continuous electricity market data, but this method can be easily
applied to any other continuous intraday electricity market. For the German
market, a specific generalized gamma distribution setup explains the overall
behaviour significantly best, especially as the tail behaviour of the process
is well covered.
