Contents
Do I have to normalize my data?
For machine learning, every dataset does not require normalization. It is required only when features have different ranges. So we normalize the data to bring all the variables to the same range.
What does cross correlation means?
Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs.
When to use time shift to normalize cross correlation?
Applying a time shift to the normalized cross-correlation function will result in a “normalized cross-correlation with a time shift of X”. This can be used to answer questions such as: “When many customers come in my shop, do my sales increase 20 minutes later?”
Do you need to normalize correlation by standard deviation?
Absolutely not, correlation analysis describe the nature of the relationship between two variables what ever the range and the measurement units of them. Thank you all for the answers! in this case, correlation is normalized by standard deviation.
Which is the best definition of cross correlation?
Definition: Cross-correlation is the comparison of two different time series to detect if there is a correlation between metrics with the same maximum and minimum values. Normalized cross-correlation is also the comparison of two time series, but using a different scoring result.
Which is an example of normalized auto correlation?
For example: “Is there weekly seasonality on a server website?” “Does the current week’s data highly correlate with that of the previous week?” Normalized auto-correlation is the same as normalized cross-correlation, but for auto-correlation, thus comparing one metric with itself at a different time.