Simplify the view to resolve the error by filtering or removing some of the dimensions.Ī forecast cannot be computed because the view contains table calculations.Ĭreate a version of the view that does not contain table calculations.Ī forecast cannot be computed because there is a measure on the Filters shelf.
TABLEAU PUBLIC NOT WORKING SERIES
To fix the forecast, aggregate the time series value at a higher level (for example, Month instead of Week) or filter the data.Ī forecast cannot be computed because the data is divided into too many rows, columns, or colors. There is too much data to compute a forecast.įorecasting is not possible when the result set from the query is too large. The value to be forecast must be a measure, and not a dimension. Some measures cannot be interpreted numerically and therefore cannot be forecast.Ī forecast cannot be computed for a dimension. The measure to forecast must be a number. Forecast measures must be on the Rows or Columns shelf, or on the Marks card. This error is returned if no measure that can be forecast is present in the view. Otherwise, you must modify the source data or use data from a different source. Aggregate your data to a higher level of detail by removing dimensions or changing the date level, for example from 'weeks' to 'months'.
Selecting Fill in missing values with zeros in the Forecast Options dialog box will not resolve this error. This error is returned if more than 40% of the data in a pane is missing. For example, if Aggregate by is set to Weeks and the date in the view is set to Months, this error occurs.Ĭhange one of the dates so that the two are compatible, or set Aggregate by to Automatic.Ī forecast cannot be computed because there are too many missing values. The date in the view must be compatible with the value of Aggregate by in the Forecast Options dialog box.
The selected 'Aggregate by' value in Forecast Options is not compatible with the visualization. For example, if both Order Date and Ship Date are in the same view, forecasting is not supported. This error is returned if there are multiple date fields in the view. This error is returned if there are fewer than four data points after trimming off unreliable or partial trailing periods which could mislead the forecast.Ī forecast cannot be computed for a time series with Null date values.Įliminate any Null values from the date field or fields in the view, either by filtering the date field or by using a less detailed date granularity (for example, by switching from months to quarters).Ī forecast cannot be computed when the view contains multiple distinct date fields. The time series is too short to forecast.Įxpand the time series in your view to include more date values.
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This error is returned if there are no dates in the view, or if the dates in the view don’t constitute a full hierarchy (for example, the date includes Year and Day, but not Month), or if they constitute a hierarchy that is not supported (for example, Year, Week, Day). If the date field is not explicitly continuous, then one of the included date levels must be Year. Error messageĪ continuous date cannot be derived from the date fields in the view.įorecasting requires a date field that can be interpreted continuously.
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The following list shows errors that can be result from invalid forecasts in Tableau, and provides advice on how to resolve them. Avoid these scenarios by setting Aggregate by to Automatic. Tableau can create a forecast when the forecast aggregation is a finer level of detail than the view's aggregation, but not when it is at a coarser level of detail even when it is finer, the two values are only compatible if there is a strict hierarchy that Tableau can use (for example, quarters can be evenly divided into three months, but months can't be evenly divided into weeks). Other problems arise when the view’s aggregation and the aggregation specified for the forecast (using the Aggregate by field in the Forecast Options dialog box) are not compatible.
For example, if the underlying granularity of the sales data is months but you aggregate by weeks, the result may be a significant number of Null values. Date aggregation can trigger a "too many Nulls" scenario when forecasting attempts to extract more data from the measure than is possible. This can lead to the "too much data" or "too little data" errors described below. If Tableau is unable to provide a forecast for your view, the problem can often be resolved by changing the Date value in the view (see Change Date Levels).įorecasting errors can result when the aggregation level of the time series (months, weeks, etc.) is either too fine or too coarse for the data to be forecast.
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