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Finding relations between variables in time series (edit)
Revision as of 19:45, 21 December 2023
, 19:45, 21 December 2023→Reasons time series analysis especially as applied to QS is hard
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.
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.
Different sampling rates need to be interpolated to be compared. 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.
Different sampling rates need to be interpolated to be compared. Window.
Machine learning also has limits on the kind of patters it can detect.
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.
Machine learning also has limits on the kind of patterns it can detect.
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.
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.