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Finding relations between variables in time series (edit)
Revision as of 02:08, 16 February 2022
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== Reasons time series analysis especially as applied to QS is hard ==
== Reasons time series analysis especially as applied to QS is hard ==
[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. 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.
Lag. What if eating pizza on one day causes heartburn the next? {{Topic Queries}}
Lag. What if eating pizza on one day causes heartburn the next?
Window. Since removing the effects of other variables makes the variable of interest's effect stand out, machine learning must be used. Common 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.
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.
{{Topic Queries}}