Changes

stats.stackexchange.com/questions/264225/finding-brief-repeated-patterns-in-a-time-series the question is not answered but in the side bar similar questions are well answered. Copulas may be important? The difference between time series and non-time series seems to be that with time series the patterns are cyclical, not a specific type of pattern/shape that happens every so often. Health tracking data seems to need both. I imagine blood sugar spike after meals but meals are not eaten at constant time. Long term trend and smoothing is covered in some Arima like models. Also changepoint which is like trend but in shorter time. And outliers? THat is like changepoint. results in a pretty visualization illustration of a single time series. If any of the apps were serious this would appear there.

stats.stackexchange.com/questions/264225/finding-brief-repeated-patterns-in-a-time-series the question is not answered but in the side bar similar questions are well answered. Copulas may be important? The difference between time series and non-time series seems to be that with time series the patterns are cyclical, not a specific type of pattern/shape that happens every so often. Health tracking data seems to need both. I imagine blood sugar spike after meals but meals are not eaten at constant time. Long term trend and smoothing is covered in some Arima like models. Also changepoint which is like trend but in shorter time. And outliers? THat is like changepoint. results in a pretty visualization illustration of a single time series. If any of the apps were serious this would appear there.

Maybe multiple moving averages www.investopedia.com/terms/g/guppy-multiple-moving-average.asp as which kernel width fits best.  

Maybe multiple moving averages www.investopedia.com/terms/g/guppy-multiple-moving-average.asp as which kernel width fits best. Maybe ecg decomposition with DWT and ICA

This all called: single subject longitudinal analysis

This all called: single subject longitudinal analysis