1,683
edits
Changes
Jump to navigation
Jump to search
mLine 80:
Line 80: − + + + + + + + + + + + + + +
Finding relations between variables in time series (edit)
Revision as of 18:23, 26 July 2023
, 18:23, 26 July 2023→What to expect from the complete analysis tool
=== What to expect from the complete analysis tool ===
=== What to expect from the complete analysis tool ===
User without experience in statistical analysis will not be able to tell the difference between correctly computed correlations and poorly computed ones. However, a genuinely complete analysis produces graphs which should include most of the following: Interpolation for irregular time series. Change point or breakpoint detection. Outlier detection. Smoothing. 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. Removal of effect of variables found to correlate with this one to show residuals.
User without experience in statistical analysis will not be able to tell the difference between correctly computed correlations and poorly computed ones. However, a genuinely complete analysis produces graphs which should include most of the following:
Interpolation for irregular time series.
Change point or breakpoint detection.
Outlier detection. Smoothing.
Removal of effect of variables found to correlate with this one to show residuals.
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.
[[Category:Data analysis]]
[[Category:Data analysis]]