The Advanced tab

Use the Advanced tab to set the scope of historical data, define a confidence interval, define where you want the forecast values to appear, and set data spreading options for writing forecast data into consolidated members. Some options on the Advanced tab are not applicable to multivariate forecasts, as noted in the option descriptions.

Some capabilities on this tab are disabled for analyst users, as described in the details.

The following options are available on the Advanced tab.

Seasonality

Not applicable to multivariate forecasts. If your model is highly seasonal, consider using a univariate forecast.

This option specifies the seasonality with which to build the model. Seasonality is when the time series has a predictable cyclic variation. For example, during a holiday period each year.

The default value is Auto-detect. Auto-detect automatically detects seasonality by building multiple models with different seasonal periods and choosing the best one.

If you switch Auto-detect off, you can specify seasonality by entering a non-negative integer, such as 0, 1, 2, 3 in the Enter seasonality interval field.

To specify a non-seasonal model, set the Enter seasonality interval to 0 or 1. A model with user specified seasonality is displayed only if the seasonal model is more accurate than all of the non-seasonal models.

Select scope of historical data used

This option is disabled for analysts, who cannot set the scope of historical data for a forecast. This capability is available only for administrators and modelers.

Select between the following two options:

• Use historical data in TM1 cube
• Use historical data in the Exploration

The default option is Use historical data in the Exploration .

In this example, the leave members of Period hierarchy start at Jan 2018 and goes on to Sept 2021. However, the exploration view starts at Jan 2019.

When the option Use historical data in the Exploration is selected, the historical data that is used for the prediction starts from Jan 2019 and includes only the members in the current Exploration view. As shown in the next image of the forecast preview, the visualization starts at Jan 2019, which is the start of the Exploration view and also used as the starting point for the history. Data for Jan 2018 - Dec 2018 is not included in the history of the prediction in this case.

Conversely, when you use the option Use historical data in TM1 cube, the leaf members of this Period hierarchy start at Jan 2018.

Based on this selection criteria, the forecast preview uses data from Jan 2018 onward to include all members in the Period dimension as the historical data. The following forecast preview image shows that the start point of the history is Jan 2018. Because more data is included as the history, and that data contains a decline, the forecast results changes.

Select confidence interval

Not applicable to multivariate forecasts, which do not predict a range with high and low values.

The certainty with which the true value is expected to be within the specified range. You can see corresponding confidence interval in a tooltip by hovering over any forecast value. The confidence interval is displayed as higher and lower bounds.

You can select three different confidence intervals: 90%, 95%, and 99%. The default is 95% and the lower and higher bound define the range at which you can be 95% confident that the true value lies within that range.

Adjust outliers

When this option is turned on, all detected outliers are corrected before performing the forecast. You can preview the effect of this by selecting a row that contains an outlier and clicking the Preview button. See Previewing with detected outliers for further information on how Adjust outliers works.

Ignore historical time periods

Ignores a specified number of data points when building the model and computing the forecasts.

You can either ignore a set of data points at the end of a time series, or ignore individual data points in different places in a time series. The ignored data points are interpolated. A linear interpolation is used to populate the ignored values. Select the ignored periods in the nested Ignore time periods pane. You can select periods either as individual points or from a starting point onwards, until the start of the forecast.

By default, no historical data is ignored. If no missing values exist, then all of the historical data is used and the first forecast point is after the last historical data point.

Ignoring data period can be useful when the data is incomplete. For example, you might be doing a forecast halfway through a month. Exclude this month from the forecast by setting Ignored historical time periods either to that specific month, or to the last data point if that month is your last.

Spread forecast values

Define the data spreading method you want to use to spread data from consolidated cells.

You can use the Proportional and Relative proportional data spreading methods. For more information, see Apply data spreading to a forecast.

Where do you want to save the predicted values

This option is disabled for analysts, who cannot specify where to save predicted values. This capability is available only for administrators and modelers.

This section lets you identify the dimension, hierarchy, and member where you would like to save your forecast values. For example, you would typically forecast from the ‘Actual’ member of the ‘Version’ dimension into the ‘Forecast’ member of the ‘Version’ dimension.

For univariate forecasts, you can also specify the members where you want to save upper-bound and lower-bound forecast values. If you want to save the upper-bound and lower-bound values for consolidations, select the Save upper and lower bound for consolidations option. These options are not available for multivariate forecasts, which do not generate upper-bound and lower-bound forecast values.