You can modify your forecasts by setting a number of period and confidence level options in the Analytics panel.
The following options are available.
- Forecast periods
- The number of steps to forecast ahead.
- The default value is Auto, which is 20% of the length of the historical data. Any missing values at the end of a particular series will also be forecast, but they will not count towards the specified number of forecast periods.
- Ignored last periods
- Ignores a specified number of data points at the end of a time series when building the model and computing the forecasts. Any missing values at the end of a non-ignored portion of a series will also be forecast. Ignored last periods value must be specified as a non-negative integer, such as: 0, 1, 2, 3.
- The default value is 0. If there are no missing values, then all of the historical data is used in model generation and the first forecast point is after the last historical data point. Up to 100 data points can be ignored.
- Ignoring the last 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 last periods to 1.
The following visualization shows a forecast that ignores September's results by setting Ignored last periods to 1.
- Confidence level
- The certainty with which the true value is expected to be within the given range. You can see corresponding confidence interval in a tooltip by hovering over any forecast value. The confidence interval is displayed as upper and lower bounds.
- You can select three different confidence levels: 90%, 95%, and 99%. The default is 95% and the lower and upper bound define the range at which you can be 95% confident that the true value lies within that range.
- Seasonal period
- 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. Auto automatically detects seasonality by building multiple models with different seasonal periods and choosing the best one.
- You can specify seasonality by entering a non-negative integer, such as: 0, 1, 2, 3 as the seasonal period.
- To specify a non-seasonal model, set the Seasonal period 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.
When visualizations have both Insights and Forecasts, you can enable both of them in the Analytics panel. Insights provide an independent set of analytic results. For more information, see Insights in visualizations.