I have six months of past sleep data, though nothing current, with sleep and wake times. I could easily augment that with other potentially relevant variables, like daily caffeine intake or whatnot.
As a starter method, I would try Adaboost. AdaBoost is nice because it is easy to implement, gives some protection against overfitting, and allows you a lot of liberty to define whatever context functions/predictors you want. Try to predict whether a given hour will be sleep or not. Use whatever information like caffeine intake you can as predictors, and use as many of them as you can dream up: AdaBoost will figure out which ones are the most important.
Wake and sleep times don’t strike me as very good data, IMO. If you had 6 months of Zeo data, that’d be real data you could try to feed into a model of some sort.
I have six months of past sleep data, though nothing current, with sleep and wake times. I could easily augment that with other potentially relevant variables, like daily caffeine intake or whatnot.
As a starter method, I would try Adaboost. AdaBoost is nice because it is easy to implement, gives some protection against overfitting, and allows you a lot of liberty to define whatever context functions/predictors you want. Try to predict whether a given hour will be sleep or not. Use whatever information like caffeine intake you can as predictors, and use as many of them as you can dream up: AdaBoost will figure out which ones are the most important.
Helpful, thanks.
Wake and sleep times don’t strike me as very good data, IMO. If you had 6 months of Zeo data, that’d be real data you could try to feed into a model of some sort.