I have a question about the implementation details for co-occurring events. Co-occurring events are quite common in practice, e.g., due to rounding of timestamps. According to the optimizer
there isn't anything stopping the model to fit a time series with co-occurring events. I wonder if this is actually being supported - meaning, will t_k^j < t be strictly followed in the actual implementation?
Hi,
I have a question about the implementation details for co-occurring events. Co-occurring events are quite common in practice, e.g., due to rounding of timestamps. According to the optimizer
https://x-datainitiative.github.io/tick/modules/generated/tick.hawkes.ModelHawkesSumExpKernLeastSq.html
there isn't anything stopping the model to fit a time series with co-occurring events. I wonder if this is actually being supported - meaning, will
t_k^j < tbe strictly followed in the actual implementation?Thanks.