My data consists of entries when an event is
True, namely, when a train crossing is down. So I will have entries within a day like so (just examples):
The state is of course
True for these entries, and the times not logged throughout a day are when the state is
False, aka traversable. (however I have a strong feeling I should explicitly pad these times in, so that I have full 24-hour cycles logged)
The data presented is a time series, based on which I wish to make predictions for coming days. I want to be able to ask "What are the chances that the crossing is down at time $t$, of day $d$?" (the second argument is important because I will have 7 different models, one for each day of the week due to the differing train traffic schedules in a week), taking into consideration the previously observed times.
This of course needs to take into account the fact that when the events happen, they have a certain length (as shown by my data). This is not inherently a classification problem because I am also interested in a confidence of the prediction, which is not as simple as taking a built model's performance parameters. Assuming though that this could be a promising way to tackle the problem, how can I build a model so that the times later on in a day don't wrongfully bias it?
Consider the simple example of some dataset, where days are represented as indexing numbers 1-7. An unsupervised classification algorithm would be biased to guess more towards saturday and sunday, because they are higher numbers (and in this case the solution is to have 7 different columns, each with boolean values instead - but I can't do anything similar to this in my case). I hope this example clears up my question a bit.