Summary
Forecasts the values of each location of a space-time cube using an adaptation of Leo Breiman's random forest algorithm. The forest regression model is trained using time windows on each location of the space-time cube.
Illustration
Usage
This tool accepts netCDF files created by the Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Features, and Create Space Time Cube from Multidimensional Raster Layer tools.
Compared to other forecasting tools in the Time Series Forecasting toolset, this tool is the most complex but includes the fewest assumptions about the data. It is recommended for time series with complicated shapes and trends that are difficult to model with simple mathematical functions or when the assumptions of other methods are not satisfied.
Multiple forecasted space-time cubes can be compared and merged using the Evaluate Forecasts By Location tool. This allows you to create multiple forecast cubes using different forecasting tools and parameters, and the tool will identify the best forecast for each location using either Forecast root mean square error (RMSE) or Validation RMSE.
For each location in the Input Space Time Cube, the tool builds two models that serve different purposes.
- Forecast model—This model is used to forecast values of the space-time cube by building a forest using the values of the time series and using this forest to forecast the values of future time steps. The fit of the forecast model to the values of the space-time cube is measured by the Forecast RMSE value.
- Validation model—This model is used to validate the forecast model and test how accurately it can forecast values. If a number greater than 0 is specified for the Number of Time Steps to Exclude for Validation parameter, this model is built using the time steps that were included and is used to forecast the values of the time steps that were excluded. This allows you to see how well the model can forecast values. The fit of the forecasted values to the excluded values is measured by the Validation RMSE value.
Learn more about the forecast model, validation model, and RMSE statistics
The Output Features parameter values will be added to the Contents pane with rendering based on the final forecasted time step.
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This tool creates geoprocessing messages and pop-up charts to help you understand and visualize the forecast results. The messages contain information about the structure of the space-time cube and summary statistics of the RMSE values and season lengths. Clicking a feature using the Explore navigation tool displays a line chart in the Pop-up pane showing the values of the space-time cube, fitted forest values, forecasted values, and confidence bounds for that location.
The Outlier Option parameter can be used to detect statistically significant outliers in the time series values at each location.
If you choose the Identify outliers option of the Outlier Option parameter, it is recommended that you provide a value for the Time Step Window parameter rather than leaving the parameter empty and estimating a different time step window at each location. For each location, the forest model uses the time steps in the first time step window to train the forecast model, and outliers are only detected for the remaining time steps. If different locations exclude different numbers of time steps for training, summary statistics such as the mean, minimum, and maximum number of outliers per time step or per location may be misleading.
Deciding how many time steps to exclude for validation is an important choice. The more time steps are excluded, the fewer time steps remain to estimate the validation model. However, if too few time steps are excluded, the Validation RMSE will be estimated using a small amount of data and may be misleading. It is recommended that you exclude as many time steps as possible while maintaining sufficient time steps to estimate the validation model. It is also suggested that you withhold at least as many time steps for validation as the number of time steps you intend to forecast, if your space-time cube has enough time steps to allow this.
Syntax
arcpy.stpm.ForestBasedForecast(in_cube, analysis_variable, output_features, {output_cube}, {number_of_time_steps_to_forecast}, {time_window}, {number_for_validation}, {number_of_trees}, {minimum_leaf_size}, {maximum_depth}, {sample_size}, {forecast_approach}, {outlier_option}, {level_of_confidence}, maximum_number_of_outliers)| Parameter | Explanation | Data Type |
in_cube | The netCDF cube containing the variable to forecast to future time steps. This file must have an .nc file extension and must have been created using the Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Locations, or Create Space Time Cube From Multidimensional Raster Layer tool. | File |
analysis_variable |
The numeric variable in the netCDF file that will be forecasted to future time steps. | String |
output_features |
The output feature class of all locations in the space-time cube with forecasted values stored as fields. The layer displays the forecast for the final time step and contains pop-up charts showing the time series, forecasts, and 90 percent confidence bounds for each location. | Feature Class |
output_cube (Optional) |
A new space-time cube (.nc file) containing the values of the input space-time cube with the forecasted time steps appended. The Visualize Space Time Cube in 3D tool can be used to see all of the observed and forecasted values simultaneously. | File |
number_of_time_steps_to_forecast (Optional) |
A positive integer specifying the number of time steps to forecast. This value cannot be larger than 50 percent of the total time steps in the input space-time cube. The default value is one time step. | Long |
time_window (Optional) | The number of previous time steps to use when training the model. If your data displays seasonality (repeating cycles), provide the number of time steps corresponding to one season. This value cannot be larger than one-third of the number of time steps in the input space-time cube. If no value is provided, a time window is estimated for each location using a spectral density function. | Long |
number_for_validation (Optional) | The number of time steps at the end of each time series to exclude for validation. The default value is 10 percent (rounded down) of the number of input time steps, and this value cannot be larger than 25 percent of the number of time steps. Provide the value 0 to not exclude any time steps. | Long |
number_of_trees (Optional) | The number of trees to create in the forest model. More trees will generally result in more accurate model prediction, but the model will take longer to calculate. The default number of trees is 100, and the value must be at least 1 and not greater than 1,000. | Long |
minimum_leaf_size (Optional) | The minimum number of observations that are required to keep a leaf (the terminal node on a tree without further splits). For very large data, increasing this number will decrease the run time of the tool. | Long |
maximum_depth (Optional) |
The maximum number of splits that will be made down a tree. Using a large maximum depth, more splits will be created, which may increase the chances of overfitting the model. If no value is provided, a value will be identified by the tool based on the number of trees created by the model and the size of the time step window. | Long |
sample_size (Optional) | The percent of training data that will be used to fit the forecast model. The training data consists of associated explanatory and dependent variables constructed using time windows. All remaining training data will be used to optimize the parameters of the forecast model. The default is 100 percent. | Long |
forecast_approach (Optional) | Specifies how the explanatory and dependent variables will be represented when training the forest model at each location. To train the forest model that will be used to forecast, sets of explanatory and dependent variables must be created using time windows. Use this parameter to specify whether these variables will be linearly detrended and whether the dependent variable will be represented by its raw value or by the residual of a linear regression model. This linear regression model uses all time steps within a time window as explanatory variables and uses the following time step as the dependent variable. The residual is calculated by subtracting the predicted value based on linear regression from the raw value of the dependent variable. Learn more about the Forecast Approach parameter
| String |
outlier_option (Optional) | Specifies whether statistically significant time series outliers will be identified.
| String |
level_of_confidence (Optional) | Specifies the confidence level for the test for time series outliers.
| String |
maximum_number_of_outliers | The maximum number of time steps that can be declared outliers for each location. The default value corresponds to 5 percent (rounded down) of the number of time steps of the input space-time cube (a value of at least 1 will always be used). This value cannot exceed 20 percent of the number of time steps. | Long |
Code sample
The following Python script demonstrates how to use the ForestBasedForecast tool.
import arcpy
arcpy.env.workspace = "C:/Analysis"
# Forecast four time steps using a random forest with detrending.
arcpy.stpm.ForestBasedForecast("CarTheft.nc","Cars_NONE_ZEROS",
"Analysis.gdb/Forecasts", "outForecastCube.nc"
4, 3, 5, 100, "", "", 100, "VALUE_DETREND")The following Python script demonstrates how to use the ForestBasedForecast tool to forecast counts of car theft.
# Forecast change in car thefts using a random forest.
# Import system modules.
import arcpy
# Set property to overwrite existing output, by default.
arcpy.env.overwriteOutput = True
# Set workspace.
workspace = r"C:\Analysis"
arcpy.env.workspace = workspace
# Forecast three time steps using a random forest based on change.
arcpy.stpm.ForestBasedForecast("CarTheft.nc","Cars_NONE_ZEROS",
"Analysis.gdb/Forecasts", "outForecastCube.nc"
4, 3, 5, 100, "", "", 100, "CHANGE",
"IDENTIFY", "90%", 4)
# Create a feature class visualizing the forecasts.
arcpy.stpm.VisualizeSpaceTimeCube3D("outForecastCube.nc", "Cars_NONE_ZEROS",
"VALUE", "Analysis.gdb/ForecastsFC")Licensing information
- Basic: Yes
- Standard: Yes
- Advanced: Yes
Related topics
- An overview of the Space Time Pattern Mining toolbox
- An overview of the Time Series Forecasting toolset
- Curve Fit Forecast
- Exponential Smoothing Forecast
- Evaluate Forecasts By Location
- Understanding outliers in time series analysis
- How Forest-based Forecast works
- Forest-based Classification and Regression
- How Forest-based Classification and Regression works
- Find a geoprocessing tool