Use Smart Functions

Smart functions represent our effort to leverage advanced statistical methods to help you gain more insights from your data with just a click of a button.

Forecasting

The forecasting function uses an autoregressive AR-X(p) model to create forecasts of future trends based on your data. Forecasting is supported for line charts:

Screenshot of a line chart that includes a forecast.

Steps:

  1. Create or open a line chart visualization in the Analytical Designer.

  2. Ensure that:

    • You are using only one metric and trending it by date.

      Screenshot of a line chart with one metric and trended over time.

    • The data contains no missing values.

  3. Under Configuration, toggle on Forecasting.

    Screenshot of the configuration tab showing the Forecasting option.

    • The number of predicted Periods must be smaller than the number of displayed data points.

    • The Confidence level determines the size of the shaded error region. A 95% confidence level means that the shaded region should be large enough to contain the predicted future data point 95% of the time.

    • Turn on Seasonality if your data is highly periodic to increase the accuracy of the forecast. For example, if your ice cream sales reliably grow every summer and plummet every winter. Note that if you enable this option, the number of predicted periods should be significantly smaller than the number of displayed data points.

    Screenshot of the resulting line chart with a forecast prediction.

Clustering

This function utilizes the BIRCH algorithm to segment your datapoints into N groups based on inherent similarities, where N is a user-defined number. Each group is color-coded for distinction. Clustering is available for line scatter plots:

Screenshot of a scatter plot that has its point highlighted using the clustering function.

Steps:

  1. Create or open a scatter plot visualization in the Analytical Designer.

    Screenshot of an ordinary scatter plot.

  2. Under Configuration, toggle on Cluster. You can adjust the number of clusters to generate.

    Screenshot of the configuration tab showing the Forecasting option.

    The clusters are highlighted:

    Screenshot of a scatter plot with clustering.

Troubleshooting

I set N number of clusters, but fewer clusters are highlighted. Why?

This behaviour occurs when the threshold parameter of the BIRCH algorithm is too high for your particular dataset. Currently, the threshold value is set to 0.03. We plan to expose this parameter, allowing you to adjust it yourself in the near future.

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