--- schema_version: "1" id: 75932203568e6ce6f83585c3932d8fd288a461c05a282c62bd1e566512352ada title: Resolving Forecasting Errors collection: tableau version: next locale: en-us portal_url: https://docs.salesforce.com/tableau/next/en-us/public/desktop/forecast_resolve_errors.md canonical_url: https://help.tableau.com/v0.0/public/desktop/en-us/forecast_resolve_errors.htm source_site: help.tableau.com content_type: documentation content_hash: 64c4dcf57ded0ed30503e83ff06dcc4447e029be9d90d384e5a6184fd8e00f09 description: Resolve forecasting errors caused by too much or too little data. --- [Skip to main content](#main-content) # Resolving Forecasting Errors 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](https://help.tableau.com/v0.0/public/desktop/en-us/dates_levels.htm)). Forecasting 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. This can lead to the "too much data" or "too little data" errors described below. Date aggregation can trigger a "too many Nulls" scenario when forecasting attempts to extract more data from the measure than is possible. 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. 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. 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). Avoid these scenarios by setting Aggregate by to Automatic. The following list shows errors that can be result from invalid forecasts in Tableau, and provides advice on how to resolve them.

Error message	Suggestion for Resolution
A continuous date cannot be derived from the date fields in the view.	Forecasting requires a date field that can be interpreted continuously. If the date field is not explicitly continuous, then one of the included date levels must be Year. 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).
The time series is too short to forecast.	Expand the time series in your view to include more date values. 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.
A forecast cannot be computed for a time series with Null date values.	Eliminate 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).
A forecast cannot be computed when the view contains multiple distinct date fields.	This error is returned if there are multiple date fields in the view. For example, if both Order Date and Ship Date are in the same view, forecasting is not supported.
The selected 'Aggregate by' value in Forecast Options is not compatible with the visualization.	The date in the view must be compatible with the value of Aggregate by in the Forecast Options dialog box. For example, if Aggregate by is set to Weeks and the date in the view is set to Months, this error occurs. Change one of the dates so that the two are compatible, or set Aggregate by to Automatic.
A forecast cannot be computed because there are too many missing values.	This error is returned if more than 40% of the data in a pane is missing. Selecting Fill in missing values with zeros in the Forecast Options dialog box will not resolve this error. Aggregate your data to a higher level of detail by removing dimensions or changing the date level, for example from 'weeks' to 'months'. Otherwise, you must modify the source data or use data from a different source.
There is no measure to forecast.	This error is returned if no measure that can be forecast is present in the view. Forecast measures must be on the Rows or Columns shelf, or on the Marks card.
The measure to forecast must be a number.	Some measures cannot be interpreted numerically and therefore cannot be forecast.
A forecast cannot be computed for a dimension.	The value to be forecast must be a measure, and not a dimension.
There is too much data to compute a forecast.	Forecasting is not possible when the result set from the query is too large. The limit is about 10,000 rows. To fix the forecast, aggregate the time series value at a higher level (for example, Month instead of Week) or filter the data.
A forecast cannot be computed because the data is divided into too many rows, columns, or colors.	Simplify the view to resolve the error by filtering or removing some of the dimensions.
A forecast cannot be computed because the view contains table calculations.	Create a version of the view that does not contain table calculations.
A forecast cannot be computed because there is a measure on the Filters shelf.	Remove the measure from the Filters shelf.
A forecast cannot be computed because Aggregate Measures is not selected.	Aggregate Measures is an option on the Analysis menu. See Data Aggregation in Tableau and How to Disaggregate Data.
A forecast cannot be computed because the view contains percent calculations.	Percentage of is an option on the Analysis menu. See Calculate Percentages in Tableau.
A forecast cannot be computed because Grand Totals or Subtotals is enabled.	These options are controlled from the Totals command in the Analysis menu. See Show Totals in a Visualization.
A multiplicative model cannot be computed because the measure to be forecast has one or more values that are less than or equal to zero.	You have created a custom model with Trend or Seasonality set to Multiplicative. Change this value, or set the Forecast Model to Automatic.
A model with multiplicative trend and additive season is not allowed because it is numerically unstable.	You have created a custom model configured as described in the error message. Change the settings for the custom model, or set the Forecast Model to Automatic.
A seasonal model cannot be computed because the time series is too short.	Expand the time series in your view to include more date values.
The selected multiplicative model cannot be computed because some of the data is too close to zero relative to the rest of the data.	You have created a custom model configured as described in the error message. Change the settings for the custom model, or set the Forecast Model to Automatic.