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Forecasting is the act of making prognoses of the future grounded on past and present data material as well as analyses of trendlines.
As a standard sample can be seen things like appreciation of some variable factors of figures and numbers.
The term prediction is often used as a synonym although it is much less specific. Both term describe statistics methodology processes as for example time series or cross sectional data of a limited quantity of the whole.
In Tableau, forecasting is used to project numbers and figures in the future in order to know the potentially values yet to come. But how does it work if with Tableau to do some forecasting for data?
Forecasting uses a technique called exponential smoothing. Forecast algorithms will try to find any regular pattern in measures that can be continued into the future.
Exponential smoothing models with trend or seasonal components are effective when the measure to be forecast exhibits trend or seasonality over that period of time on which the forecast is based. Trend is a tendency in the data to increase or decrease over time. Seasonality is a repeating, predictable variation in value, such as an annual fluctuation in temperature relative to season.
In general, the more data points you have in your time series, the better the resulting forecast will also be.

Model Types
In the Forecast Options dialog box, you can choose the model type.
The Automatic setting is typically optimal for views in most cases. If you choose Custom, then you can also specify the trend and season characteristics independently, choosing None, Additive, or Multiplicative:

An additive model is one in which the contributions of the model components are summed, whereas a multiplicative model is one in which at least some of the component contributions are multiplied. Multiplicative models can significantly improve forecast quality for data where the trend or seasonality is affected by the level (magnitude) of the data given:

Keep in mind that you do not need to create a custom model to generate a forecast that is multiplicative: the Automatic setting can also determine if a multiplicative forecast is appropriate for your data. However, a multiplicative model cannot be computed when the measure to be forecast has one or more values that are less than or equal to zero.
Forecasting with Time
When you are forecasting with a date, there can be only one base date in the view. Part dates are supported, but all parts must refer to the same underlying field. Dates can be on Rows, Columns, or Marks (with the exception of the Tooltip target).
Tableau supports three types of dates, two of which can be used for forecasting:
Truncated dates and reference a particular point in history with its specific temporal granularity, such as May 2018. They are usually continuous, with a green background in the view. Truncated dates are valid for forecasting.

  • Date parts refer to a particular member of a temporal measure such as February. Each date part is represented by a different, usually discrete field (with a blue background). Forecasting requires at least a Year-date-part. Specifically, it can use any of the following sets of date parts for forecasting:
  • Year
  • Year + quarter
  • Year + month
  • Year + quarter + month
  • Year + week
  • Custom: Month/Year, Month/Day/Year

Exact dates refer to a particular point in history with maximum temporal granularity such as March 1, 2014 at 16:51:12.0. Exact dates are invalid for forecasting. It is also possible to forecast without a date.


charting/forecasting.txt · Last modified: 2020/08/20 13:27 (external edit)