Changes

Really strong relationships will be detected even through most of these problems.  

Really strong relationships will be detected even through most of these problems.  

[http://www.tylervigen.com/spurious-correlations Spurious Correlations] mostly shows that if two things are trending in one direction and are checked for correlation they will show a very significant correlation. Practice effect is a subset. Another is one instance of an event increases the chances of the same event happening soon after. Economists suggest unit root.

[http://www.tylervigen.com/spurious-correlations Spurious Correlations] mostly shows that if two things are trending in one direction and are checked for correlation they will show a very significant correlation. Practice effect is a subset. Another is one instance of an event type A increases the chances of the same event type happening soon after. Economists suggest unit root.

Effects on target variable from outside known variables. In non time series this is compensated for with RCT but in time series such an effect may last a while and coincide with an intervention causing very false results. This problem makes baseline data gathering more difficult and also necessary. Sometimes a baseline will show that this issue does not occur for a particular target variable. Alternatively experimenter could compensate by strictly controlling all possible sources of variance.   

Effects on target variable from outside known variables. In non time series this is compensated for with RCT but in time series such an effect may last a while and coincide with an intervention causing very false results. This problem makes baseline data gathering more difficult and also necessary. Sometimes a baseline will show that this issue does not occur for a particular target variable. Alternatively experimenter could compensate by strictly controlling all possible sources of variance.   

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?  

Few positive instances but they are important. Went to a specific restaurant twice got sick soon after twice. Only ever got sick with similar symptoms five times. Or. Two large rare humps happen almost one after the other, similar to previous example if treated as events, adding the fact that lots of samples showing their similarity in shape too.  

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

 

Interpolate. Variables have different sampling rates so need to be interpolated to be compared.

Different sampling rates need to be interpolated to be compared. Window.

Types of data. [Exercised] is an event with specific occurrence moment and length while [tired] is a vaguer value user could use to try to describe feelings past 4 hours.  

Since removing real effects of other variables on target variable makes the variable of interest's effect stand out, machine learning will be used. Basic approach would be to bin predictor variables multiple ways based on time from effect being checked, mean or other aggregator method and window of the aggregator.  

All the [[Issues with Self Report]] .    

Machine learning also has limits on the kind of patterns it can detect.  

Few positive instances but they are important. Went to a specific restaurant twice got sick soon after twice. Only ever got sick with similar symptoms five times. Or. Two large rare humps happen almost one after the other, similar to previous example if treated as events, adding the fact that lots of samples showing their similarity in shape too.  

Types of data. [Exercised] is an event with specific occurrence moment and length while [tired] is a vaguer value user could use to try to describe feelings past 4 hours.    

Since removing real effects of other variables on target variable makes the variable of interest's effect stand out, 'machine learning' needs to be used. Basic approach would be to bin predictor variables multiple ways based on time from effect being checked, mean or other aggregator method and window of the aggregator.  

All the [[Issues with Self Report]] .    

Machine learning also has limits on the kind of patterns it can detect.  

=== What to expect from the complete analysis tool ===

=== What to expect from the complete analysis tool ===