Strategy One

Tips and Best Practices

Optimize Forecast Line Chart Interpretation

Get the most out of your forecast line chart visualization!

  • Hover for Insights Hover your cursor over data points on the forecast line chart to reveal tooltips. These tooltips provide detailed information about forecasted values, as well as upper and lower bounds.

  • Info icon Hover over the info icon to see information about the underlying model and its parameters.

  • Expand for Clarity If you need more detailed information and the ability to display the entire set of data points, expand the visualization. This enhances the readability of the chart.

Forecast Analysis FAQs

What time series forecast method is used in the current release?

The time series forecast method used in our current release is called exponential smoothing. This method supports various non-damped models described on the OTexts site. You can refer to the statsmodel Python module (statsmodels.tsa.exponential_smoothing.ets.ETSModel - statsmodels 0.14.0) for more technical details.

What are the default hyper-parameters used for forecasting?

If not specified by the user, the following default hyper-parameters are used:

  • h value Five data points forward

  • Season model additive (adjustable via Auto)

  • Trend model additive (adjustable via Auto)

  • Seasonality length auto (adjustable via Auto)

  • Confidence interval 95% (adjustable via Auto)

The values for the season and trend models can be set to none, multiplicative, or additive via Auto. You can forecast the next three months of sales with a multiplicative trend model.

Can hyper-parameters be adjusted for forecasting?

Yes, you can adjust hyper-parameters for forecasting through Auto. While these hyper-parameters are not exposed in the Format panel in dashboard authoring, you can fine-tune them using Auto.

What types of attributes and metrics are required for successful forecasting?

To successfully forecast data, you need either:

  • Single attribute of a date or datetime type, plus a single metric of interest (recommended)

  • Single attribute of an integer type, plus a metric of interest (when the date/datetime attribute is unavailable for time series forecasting)

Is missing data handled in forecasting?

Yes, the algorithm can automatically fill in missing data if missing data is less than or equal to 90% of the overall data.

What time-based attribute forecasting levels are supported?

Time-based attribute forecasting is supported for the following levels of data: Daily, Weekly, Monthly, Quarterly, and Yearly.

How does integer forecasting work?

For integer forecasting, the algorithm generates the next integer in the sequence. If an integer is used to represent time (such as 202101, 202102), it is not converted to date/time format, and the forecast adds consecutive integers as future points.