# Predicting the next occurrence based on binary

I have no experience in statistics or machine learning. I have a True/False binary array describing occupation of open public spaces

+---------------------+
|  index   |  Value   |
+---------------------+
| 0        |  True    |
| 1        |  True    |
| 2        |  False   |
| 3        |  False   |
| 4        |  False   |
| 5        |  True    |
| 6        |  False   |
| 7        |  False   |
| 8        |  True    |
| ...      |  ...     |
+---------------------+


Without getting into dependent variables and domain specific heuristics, is there (or maybe more than one) a simple method to do infer the next False in python?

Ideally in pure python or using packages written in pure python.

My question is somewhat similar to this one, but I have more of a time series (i think).

• Do you have anything that could be used as features (i.e. potential clues)? For instance I could imagine that the occupation of some public spaces may depend on the time of day, day of the week, weather, etc. Also does your index represent regular points in time, i.e. every hour or day? – Erwan Feb 1 at 13:22
• Time of the day is mostly what i have, that is the index of my array (sampling is every 10 mins i think). Can this be features? There is lots of other information that is related like you say weather etc. but at this stage i'm only asked to use this dataset and not even between spaces (so look at each array in isolation) – developer1 Feb 1 at 14:34
• Well it looks a bit like a sequence labeling problem (en.wikipedia.org/wiki/Sequence_labeling), but I'm not sure that's the best way to predict the next label. If you have only the sequence of labels themselves you could also consider language modeling. – Erwan Feb 1 at 18:26