I have a dataset with timestamps and event values (true or false -- these are based on sensor data which detect room occupancy). I'd like to build a model that would take a timestamp as an input and it would predict the following 24 hours of event data (1 event for every hour). Note that the signal shows a strong correlation with day of week and hour of day.
The inputs can be derived from the timestamp alone, so you can obtain attributes such as month, hour, minute, day of week as well as additional variables that, for example, may indicate a period of high activity (based on prior analysis).
The outputs have to be a sequence of event values (true/false or 1/0), 1 for each of the following 24 hours.
Note that unlike a time series problem, (1) the response variable is a class and (2) there may be a substantial gap between the last training point and new data for scoring (we don't actually receive the last N days of activity data before having to score a new data point). To treat this as a time series problem, I'd have to ignore month and year information and shift it back to the latest point in the data with matching hour of day and day of week, but this approach feels hacky.
The simple approach to tackle this problem would be to build 24 separate classification models, one for each prediction hour, but is there a better way?
In addition to the aforementioned set of classification models, I'm using a simple benchmark where I aggregate even rates over day of week and hour of day -- if the rate is above 50% at any point, I'd classify it as true, otherwise false.
I originally thought of using methods such as an LSTM neural network or a CRF model, however these approaches rely on a sequence of inputs to generate the next value in that sequence, whereas what I'm looking for is a model that could generate a sequence of outputs based on non-sequential data.
Perhaps there is another way to look at this problem. Perhaps there is no advantage in thinking of the response as a sequence. Either way, I look forward to hearing your suggestions.