Is there some sort of pattern detecting thing … that could automatically take a time series data and predict the next values based on an unknown, complex model?
There are lots of tools like this. The problem is getting enough clean data. If you don’t have enough data, you won’t be able to use a complex model without overfitting. If the underlying phenomena (i.e. your sleep cycles) is itself complex, then you won’t be able to obtain a good description. On the other hand, it can’t hurt to try. How much data do you have?
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.
There are lots of tools like this. The problem is getting enough clean data. If you don’t have enough data, you won’t be able to use a complex model without overfitting. If the underlying phenomena (i.e. your sleep cycles) is itself complex, then you won’t be able to obtain a good description. On the other hand, it can’t hurt to try. How much data do you have?
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.