Changes

== Reasons time series analysis especially as applied to QS is hard ==

== Reasons time series analysis especially as applied to QS is hard ==

[https://forum.quantifiedself.com/t/my-baseline-network-physiology-10-days-of-eeg-egg-ekg-cgm-temperature-activity-and-food-logs/5671/19 Wavelet coherence] is one potential solution.

[https://forum.quantifiedself.com/t/my-baseline-network-physiology-10-days-of-eeg-egg-ekg-cgm-temperature-activity-and-food-logs/5671/19 Wavelet coherence] is one potential solution.

[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 increases the chances of the same event 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  

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. 

Lag. What if eating pizza on one day causes heartburn the next?  

Lag. What if eating pizza on one day causes heartburn the next?