Available with Image Analyst license.
Summary
Computes a forecasted multidimensional raster object using the output trend raster from the GenerateTrend function.
Discussion
For more information about how this function works, see the Predict Using Trend raster function.
The referenced raster dataset for the raster object is temporary. To make it permanent, you can call the raster object's save method.
Syntax
PredictUsingTrend (raster, {dimension_definition_type}, {dimension_values}, {start}, {end}, {interval_value}, {interval_unit})
Parameter | Explanation | Data Type |
raster | The input trend raster from the GenerateTrend function. | Raster |
dimension_definition_type | Specifies the method to be used to provide prediction dimension values.
(The default value is BY_VALUE) | String |
dimension_values [dimension_values,...] | The dimension value or list of values to be used in the prediction. The format of the value must match the format of the dimension in the input multidimensional raster. If the trend raster was generated for the StdTime dimension, the format will be YYYY-MM-DDTHH:MM:SS, for example, 2050-01-01T12:00:00. This argument is required when the dimension_def parameter is set to BY_VALUE. (The default value is None) | String |
start | The start date, height, or depth of the dimension interval to be used in the prediction. The format of the value must match the format of the dimension in the input multidimensional raster. (The default value is None) | String |
end | The end date, height, or depth of the dimension interval to be used in the prediction. The format of the value must match the format of the dimension in the input multidimensional raster. (The default value is None) | String |
interval_value | The number of steps between two dimension values to be included in the prediction. The default value is 1. (The default value is 1) | Double |
interval_unit | Specifies the unit that will be used for the interval_value argument. This only applies when the dimension of analysis is a time dimension.
(The default value is HOURS) | String |
Data Type | Explanation |
Raster | The output raster. |
Code sample
This example generates the forecasted NDVI values for each month in the year 2025.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
# Set the local variables
in_raster = "C:/Data/NDVI_Trend.crf"
dimension_def = "BY_INTERVAL"
dimension_values = "None"
start = "2025-01-01T00:00:00"
end = "2025-12-31T00:00:00"
interval_value = 1
interval_unit = "MONTHS"
# Apply PredictUsingTrendRaster function
predicted_raster = arcpy.ia.PredictusingTrend(in_raster, dimension_def,
dimension_values , start, end, interval_value, interval_unit)
# Save the output
predicted_raster.save("C:/arcpyExamples/outputs/predicted_NDVI.crf")