Changes

Most personal science projects require finding relationships between different variables of the type 'time series'<ref>Core-Guide_Longitudinal-Data-Analysis_10-05-17.pdf (duke.edu)</ref>. An example could be the question "does my daily chocolate consumption correlate with my daily focus score?".   

Most personal science projects require finding relationships between different variables of the type 'time series'<ref>Core-Guide_Longitudinal-Data-Analysis_10-05-17.pdf (duke.edu)</ref>. An example could be the question "does my daily chocolate consumption correlate with my daily focus score?".   

You could do experiments if you control everything rigidly or if the effects are strong and quick, like less than a week. Old data may be useable as Baseline. If both block (like 2 weeks) and daily mixed (random intervention every day)  produce the same results then issues of time series are probably not in your experiment.     

You could do experiments if you control everything rigidly or if the effects are strong and quick, like less than a week. Old data may be useable as Baseline and a baseline may rule out some issues. If both block (like 2 weeks) and daily mixed (random intervention every day)  produce the same results then issues of time series are probably not in your experiment.     

Finding more complicated relationships require better statistical tests and algorithms and data science skills. Apps that would do this automatically or at least easily are not yet available. See below. Most internet resources treat time series as (regular cyclical) series, which is not useful as most of the tracked variables have irregular patterns and don't even have a regularly cyclical component.   

Finding more complicated relationships require better statistical tests and algorithms and data science skills. Apps that would do this automatically or at least easily are not yet available. See below. Most internet resources treat time series as (regular cyclical) series, which is not useful as most of the tracked variables have irregular patterns and don't even have a regularly cyclical component.   

==== Sonar [https://www.sonarhealth.co sonarhealth.co] ====

==== Sonar [https://www.sonarhealth.co sonarhealth.co] ====

Customizable aggregation and syncing like weigh fitbit twice as much as apple watch or average steps instead of sum.

Customizable aggregation and syncing like weigh fitbit twice as much as apple watch or average steps instead of sum.

[[Gyroscope]]

[[Gyroscope]]

Export from Apple Health<ref>github.com/Lybron/health-auto-export</ref> (no analysis)

Export from Apple Health<ref>github.com/Lybron/health-auto-export</ref> (no analysis)

== List of very technical tools ==

== List of very technical tools ==

Build up. What if it takes two days of eating pizza to cause heartburn?  

Build up. What if it takes two days of eating pizza to cause heartburn?  

Bin. Window. Smooth. Variables only make domain sense as aggregate over some time. Variables have a really high sampling rate.   

Bin. Window. Smooth. Variables only make domain sense as aggregate over some time. Variables have a really high sampling rate.   

Cycles decomposition using a model like ARIMA. Ex. kayak season is in the summer or lunch is at exactly 1pm.   

Cycles decomposition using a model like ARIMA. Ex. kayak season is in the summer or lunch is at exactly 1pm.   

Detection of repeated shapes implying similar events that are not cyclical; like dinner is anywhere between 4pm and 10pm and causes a particular 2 hour spike in glucose.

Detection of repeated shapes implying similar events that are not cyclical; like dinner is anywhere between 4pm and 10pm and causes a particular 2 hour spike in glucose.

== References ==

== References ==

[[Category:Data analysis]]

[[Category:Data analysis]]