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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 ecg decomposition with DWT and ICA
 
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
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This all called: single subject longitudinal analysis . how about Temporal Dynamics?
    
== research chat suggests  ==
 
== research chat suggests  ==
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look at the extremes of the predictor variables; like three day long terrible sleep and three day long good sleep
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look at the extremes of the predictor variables; like three day long terrible sleep and three day long good sleep and compare cognitive ability
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== another aggregator ==
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www.opencures.org
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apps.apple.com/us/app/this-that/id1660363624
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== Series analysis is not it ==
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Old concept about constantly doing old school statistical testing to know when there is enough data to stop something like a clinical trial.
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