One Tool Example
ETS has a One Tool Example. Visit Sample Workflows to learn how to access this and many other examples directly in Alteryx Designer.
Use ETS to estimate a univariate time series forecasting model using an exponential smoothing method. Exponential smoothing is a commonly used forecasting approach that is based on a weighted average of past observations, with the weights declining in size for more distant past values (the weights are said to follow an exponential decay function). The tool is able to account for three time-series components: level, trend, and seasonality. The tool can use fully automated methods to model the three components in the "best way" based on statistical criteria, or you can specify the underlying methods used. An excellent discussion of the methods used can be found in Chapter 7 of Hyndman and Athanasopoulos's online book Forecasting: Principals and Practice.
This tool uses the R tool. Go to Options > Download Predictive Tools and sign in to the Alteryx Downloads and Licenses portal to install R and the packages used by the R tool. Visit Download and Use Predictive Tools.
Configure the Tool
Required Parameters Tab
Use the Required parameters tab to set the mandatory controls for the exponential smoothing forecasting model.
- Model name: Each model needs to be given a name so it can later be identified. Model names must start with a letter and may contain letters, numbers, and the special characters period (".") and underscore ("_"). No other special characters are allowed, and R is case sensitive.
- Select the target field: Select the field from the data stream you want to forecast. Measurements for this field need to be made at regular time intervals (for example, daily, monthly, quarterly, etc.).
- Target field frequency: Choose the time interval for the observations of the target field.
Model Type Tab
Use the Model type tab to set optional controls that impact how time and trends are handled in your model.
- Error type: This option controls how the effect of the nearest prior periods is modeled. Choices are Auto (the default), Additive, and Multiplicative. If Auto is used, both additive and multiplicative specifications are estimated and a statistical information criteria is used to select between models. The estimated parameter that gives the relative weight between more recent and more distant past values in the output is alpha.
- Trend type: This controls how the effect of trend is modeled. The choices are Auto (the default), Additive, Multiplicative, and None. If Auto is used, both additive and multiplicative specifications, along with models with no correction from trend, are considered, and a statistical information criteria is used to select between models. The estimated parameter that gives the relative weight between more recent and more distant trend values in the output is beta.
- Trend dampening: This option controls the extent to which the effect of recent trend effects are reduced (dampened). The choices are Auto (the default), Yes, and No. The Auto option considers models both with and without dampening, and selects the best one based on a statistical information criteria. The estimated parameter phi in the output (only for cases where trend dampening is included best model) indicates the extent to which the forecast trend has been dampened.
- Seasonal type: This controls how seasonal effects are modeled. The choices are Auto (the default), Additive, Multiplicative, and None.
Other Options Tab
Use the Other options tab to set the optional controls for criteria, transformations, and periods.
- Information criteria for model selection: The criteria used to compare different models and select the best model. The choices provided are Auto (the default), the Akaike Information Criterion (AIC), the Corrected Akaike Information Criterion (AICc) or the Bayesian Information Criteria (BIC). If the Auto option is selected then the AICc is used if there are 48 or fewer observations of the target, otherwise, the AIC is used.
- Use a Box-Cox transformation: If this option is selected, the user can provide a value of lambda (falling between 0 and 1) to do a Box-Cox transformation of the target field. If this option is selected, multiplicative specifications of the three time-series components are not considered.
- Series starting period (optional): This option allows the user to specify the starting period of the time series, which will be reflected in the decomposition and forecast plots. If Target Field Frequency is set to Hourly, Daily (all days), or Daily (weekdays only), this option is not available.
- The number of periods to include in the forecast plot: This option results in a plot that contains the original data and a number of forecast future points (along with 80% and 95% confidence intervals around the forecast points). You can specify the number of periods that should be forecast into the future for the plot.
- Select Week Format: This allows you to choose a method to specify work weeks. These options relate to what constitutes the first week of the year, and what day of the week a week begins on.
- US: Sunday is the first day of the week.
- UK: Monday is the first day of the week.
- ISO8601: Monday is the first day of the week.
Graphic Options Tab
Use the Graphics Options tab to set the optional controls for the output.
- Plot size: Select inches or centimeters for the size of the graph.
- Graph resolution: Select the resolution of the graph in dots per inch: 1x (96 dpi), 2x (192 dpi), or 3x (288 dpi).
- Lower resolution creates a smaller file and is best for viewing on a monitor.
- Higher resolution creates a larger file with better print quality.
- Base font size (points): Select the size of the font in the graph.
View the Output
Connect a Browse tool to each output anchor to view results.
- O anchor: Consists of an output stream containing the ETS model object that can be used for both point forecasts and a user-specified percentile confidence interval surrounding those forecasts.
- R anchor: Consists of the report snippets generated by the ETS tool: a statistical summary, autocorrelation diagnostic plots, and forecast plots.
- I anchor: An interactive HTML dashboard consisting of plots and metrics. Select different graphical elements to interact with the visualizations to reveal more information, values, metrics, and analytics.
Expected Behavior: Plot Calculations
The forecast plot uses a default date for calculations if any of these configuration settings are used:
- Target Field Frequency is set to Hourly, Daily (all days), or Daily (weekdays only).
- Target Field Frequency is set to Weekly, Monthly, Quarterly, or Annually and the Series starting period is not set.
The default date used might vary, making the calculation appear random.