Starting from:

CA$9500

TIME SERIES Outlier and Anomaly Detection Template

With this template, you are guaranteed to find anywhere between 1 and 13 time series patterns if you are doing analysis on 12 months (columns) of data. The number of patterns automatically vary with the number of columns being analyzed. Download and view the FREE SAMPLE OUTPUT - TIME SERIES Outlier and Anomaly Detection Template for an example of what this might look like.

STEP 1: Purchase and download this TIME SERIES Outlier and Anomaly Detection Template.

STEP 2: Summarize your customer, product, store, or other data by month and paste it into the grey-shaded area of the TIME SERIES Outlier Template. Download and view the FREE SAMPLE OUTPUT - TIME SERIES Outlier and Anomaly Detection Template for an example of what this might look like.

STEP 3: Click the 'Time Series Boxplot Analysis!' button in column AB of the 'data' tab in the TIME SERIES Outlier Template. Download and view the FREE SAMPLE OUTPUT - TIME SERIES Outlier and Anomaly Detection Template for an example of what this might look like.

STEP 4: Use the following link to Embed your TIME SERIES Outlier Template Excel workbook on your web page or blog from SharePoint or OneDrive for Business and share it with others in the Cloud.

 

Great for Data Analysis Toolpak users. You may need to do the following before using this template:


- Enable or disable macros in Microsoft 365 files
- Unblock macros from downloaded files

 

Notes:

- Each time series pattern segment is assigned a Letter Grade ranging from A+ to F: an A+ rating indicates a pattern that remains stable and consistently low throughout a 12-month period. An F Letter Grade indicates consistently, unusually high values throughout a 12-month period.

 

- An excellent alternative to the K-Means or Hierarchical clustering algorithms commonly used for database segmentation analytics: you save a lot of time finding and interpreting the optimal number of clusters.

- The box & whisker plot algorithm is a more stable database segmentation solution that relies on the median for identifying segments; K-Means and Hierarchical clustering use the mean (average) to define segments making it sensitive to outliers and anomalies.

 

- Two different 12-month datasets run through this template may result in a unique number of time series segments for each.

 

- Comparing the same dataset for two separate 12-month periods can result in a different number of time series patterns for both.

 

- You can view the FREE CASE STUDY: Humber River Water Levels Time Series Box Plot Data Analysis for en example of how this algorithm found 4 time series patterns when run on Humber River water levels.

More products