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Vizlib Line Chart - Advanced Analytics: Forecasting


The Vizlib Line Chart introduces new forecasting capabilities in Qlik Sense with just one click away without the need for coding or a complex installation.

What is a forecast?

Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends.

Why use forecasting?

Using statistical modeling techniques, you can analyze the patterns in your past data and project those patterns to determine map future data points. Business need to make future decisions based on existing data. Here are some of the areas where you can make use of forecasting:

  • Financial Planning
  • Price Stability
  • Demand Forecasting
  • Supply Chain Management
  • Sales Planning

Advanced Analytics: Vizlib Line Forecasting

The Vizlib forecasting feature has a number of simple parameters to help you project forecasts and adjust your forecasting model to your data.

Enable Forecast Calculation. 

Period Definition

  • The Period Definition defines the number of data points that constitute a period. With days as data points, 7 would define a weekly period, 91 a quarterly period. With months as data points, 12 would represent an annual period.  

Number of Training Periods

  • This is the number of periods used to take into account when calculating the forecast.

Number of Forecasting Data Points

  • This is the number of data points to forecast. The constraint for the maximum value is the period length × number of training periods x 0.5. The training period is the amount of source data used to calculate the forecast

  • Forecast Start Indicator
Enable Forecast Reference Line Indicator to add a visual cue to indicate where the Forecasting period in your line chart begins.

Calculation Model

The statistical model used for forecasting is the Holt-Winters method also known as Triple Exponential Smoothing (For more information on the model used Holt-Winters Forecasting for Dummies).

The Holt-Winters method analyzes the trend, average and seasonality of historical data to provide a statistical prediction for the future.

Further to this, Vizlib introduces different calculating models, starting from Quick: Minimal, to Intense. The lighter the model, the quicker the calculation time but the lower the resolution of the prediction. The heavier the model, the longer it takes to calculate, but the smoother the prediction will be.

1. light

2. economic

3. standard

4. heavy

Vizlib is the author of this solution article.

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