Time-Series Correlation

Use Cases

The Time-Series Correlation discovers parameters that have high correlation with the selected parameter, taking the burden of discovery off of you (compared to tools like the Correlation Heatmap, which requires active parameter selection). This tool compares the selected parameter with all parameters except those that you excluded, ranks the pairs by the magnitude of their correlation coefficients, and provides details about the top 10 pairs. The Time-Series Correlation is most helpful early in the exploratory process to surface high correlations that you want to investigate in more detail. This is a good tool to lead to Curve Fit Analysis.


The Time-Series Correlation is a multivariate analysis that you can perform on continuous fields on the Cycle and Part models. Using the Cycle model, the Time-Series Correlation can investigate parameters from the same machine type with high granularity. Using the Part model lets you compare fields across different machines, but with lower granularity (the Part model is limited by the serial number or other identifier used to construct the data model).


The Time-Series Correlation allows you to select one parameter of interest and any number of other parameters to analyze. The Time-Series Correlation computes the Pearson-R correlation coefficient between the selected parameter and each of the others. The first plot shows all parameters over time for the selected date range. For each of the top 10 pairs (ranked by correlation coefficient), the tool plots the two parameters over time and presents summary statistics including Count, Standard Deviation, Minimum, and Maximum.