Create an analysis

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read

    • Create analyses to model different outcomes based on specific interventions or changes. You can generate forecasts for each analysis and use that information to compare the different outcomes and better understand the impact of potential interventions.

    Before you begin

    Role required: sn_esg.program_manager

    Procedure

    1. Navigate to All > Operational Sustainability Management > Operational Sustainability Workspace > Lists > Analysis contexts.
    2. Select an analysis context record that you want and navigate to the Analysis tab.
    3. Select New.
    4. On the form, fill in the fields.
      Table 1. Create New Analysis form
      Field Description
      Name Name for the analysis. For example, Best case.
      Forecast Method Method used to generate forecasting data.
      • Auto

        By default, the instance chooses the best method for you automatically, based on the fit of the method. For more information, see Automatic selection of forecast methods.

      • Linear

        Generates a linear regression forecast based on the historical scores, using constant and trend as explanatory variables.

      • Seasonal

        Generates a linear regression forecast based on the historical scores, using seasonal dummies as explanatory variables. A 'season' for this analysis is one period.

      • Seasonal Trend

        As Seasonal, but includes a trend as an explanatory variable.

      • Seasonal Trend Loess (STL)

        Generates a seasonal forecast based on a best-fit function. This method fits a trend, a season, and a random noise process to the data using an exponentially weighted moving average approach. The forecast is based on the full data set, with more weight given to more recent observationsA 'season' for this analysis is one period.

      • Random Forest (RF)

        Creates a combination of decision trees where the predictions produced by these trees are averaged to get a single prediction. The randomness comes from each tree being built from a random subset of the available data and inputs.

      • Autoregressive (AR)

        The autoregressive (AR) model forecasts future values of an indicator by using a linear combination of a trend, seasonal dummies, and past values. Like the Random Forest (RF) model, the AR model checks for the best number of lags. However, the AR model relates current to past values linearly, whereas the RF model is non-linear.

      For more information, see Forecast methods.
      State State of the analysis record.
      • Draft
      • In progress
      • Published
      Description Description of the analysis.
    5. Select save.
    6. Optional: Navigate to the Emission factor analysis tab.
      This tab is only available if a formula associated with the analysis uses an emission factor.
      1. Enter the location in the Emission factor location field.
      2. Select Save.
      This step is only required if a formula associated with the analysis uses an emission factor.
    7. Select Forecast.
      Your analysis record has been created. A Forecast tab has been added where you can view the generated standard forecast.

    What to do next

    Adjust parameters to model different outcomes based on specific interventions or changes. For more information, see Adjust parameters.