In the following paper, we examine the problem of the intensity estimation of transaction arrivals in the intraday electricity markets. Assuming the inter-arrival times distribution and considering multiple intensity function types, we utilize a maximum likelihoodestimation. A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models. We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L . 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  .
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 .
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In the following paper, we examine the
We examine the novel
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intensity
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arrivals
arrival processes
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Assuming the inter-arrival times distribution and considering multiple intensity function types, we utilize a maximum likelihoodestimation. A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models.
We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood.
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forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L
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
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. 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