Available with Image Analyst license.
Overview
Estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster.
Notes
Supported multidimensional raster datasets include netCDF, GRIB, HDF, and Esri's CRF. Multidimensional mosaic datasets are also supported.
The output is a multidimensional raster layer in which each slice is a multiband raster containing information about the trend line. If you are analyzing the trend for a single variable in a dataset containing a single dimension (for example, time), there will be a single slice in the output dataset. If you are analyzing a single variable for a dataset containing multiple dimensions (for example, time and depth), each slice will contain trend information for each dimension value.
The output trend raster generated with this function is used as the input to the Predict Using Trend function.
There are three trend line options for fitting a trend to the variable values along a dimension: linear, polynomial, and harmonic. The three trendfitting options are described below.
For linear trend analysis, the output contains threeband rasters, where the following occurs:
 Band 1 = Slope
 Band 2 = Intercept
 Band 3 = Root Mean Square Error (RMSE) or the error around the line of best fit
For polynomial trend analysis, the number of bands in the output depends on the polynomial order. Secondorder polynomial fitting produces a fourband raster, where the following occurs:
 Band 1 = Polynomial_2
 Band 2 = Polynomial_1
 Band 3 = Polynomial_0
 Band 4 = RMSE
Thirdorder polynomial fitting produces a fiveband raster, where the following occurs:
 Band 1 = Polynomial_3
 Band 2 = Polynomial_2
 Band 3 = Polynomial_1
 Band 4 = Polynomial_0
 Band 5 = RMSE
For harmonic trend analysis, the number of bands in the output depends on the harmonic frequency. When the frequency is set to 1, the output is a fiveband raster, where the following occurs:
 Band 1 = Slope
 Band 2 = Intercept
 Band 3 = Harmonic_sin1
 Band 4 = Harmonic_cos1
 Band 5 = RMSE
When the frequency is set to 2, the output is a sevenband raster, where the following occurs:
 Band 1 = Slope
 Band 2 = Intercept
 Band 3 = Harmonic_sin1
 Band 4 = Harmonic_cos1
 Band 5 = Harmonic_sin2
 Band 6 = Harmonic_cos2
 Band 7 = RMSE
The Length of Cycle parameter for harmonic trend analysis is used to indicate the number and length of cycles you expect to see in your data throughout a day or year. For example, if you expect your data to go through two cycles of variation in one year, the length of cycle will be 182.5 days or 0.5 years. If you have temperature data collected every three hours, and there is one cycle of variation per day, the length of cycle is one day.
The Frequency parameter for harmonic trend analysis is used to describe the harmonic model to fit to the data. If the frequency is set to 1, a combination of linear and the first order harmonic curve will be used to fit the model. If the frequency is 2, a combination of linear, first order harmonic curve, and second order harmonic curve will be used to fit the data. If the frequency is 3, an additional third order harmonic curve will be used to model the data, and so on.
Model goodnessoffit statistics can be generated as optional outputs. The root meansquare error (RMSE), Rsquared, and the trend slope pvalue can be calculated and symbolized. Symbolize the output trend raster layer using the RGB symbology and specify the statistics as the red, green and blue bands.
Parameters
Parameter  Description 

Raster  The input multidimensional raster. 
Dimension Name  The dimension along which a trend will be extracted for the variable or variables selected in the analysis. 
Regression Type  Specifies the type of line to be used to fit to the pixel values along a dimension.

Harmonic Frequency  The frequency number to use in the trend fitting. This parameter specifies the frequency of cycles in a year. The default value is 1, or one harmonic cycle per year. This parameter is only included in the trend analysis for a harmonic regression. 
Length of Cycle  The length of periodic variation to model. The unit is days, regardless of the time unit of the input data. For example, leaf greenness often has one strong cycle of variation in a single year, so the cycle length is 365.25, even if the input data is monthly greenness. Hourly temperature data has one strong cycle of variation throughout a single day, so the cycle length is 1. The default length is 365.25 days for data that varies on an annual cycle. 
Cycle Unit  Specifies the time unit to be used for the length of a harmonic cycle.

Polynomial Order  The polynomial order number to use in the trend fitting. This parameter specifies the polynomial order. The default value is 2, or secondorder polynomial. This parameter is only included in the trend analysis for a polynomial regression. 
Ignore NoData  Specifies whether NoData values are ignored in the analysis.

RMSE  Specifies whether to generate the root mean square error (RMSE) of the trend fit line.

RSquared  Specifies whether to calculate the Rsquared goodnessoffit statistic for the trend fit line.

Pvalue of Slope Coefficient  Specifies whether to calculate the pvalue statistic for the slope coefficient of the trend line.

Regression types
The regression equation for each trend option is listed below.
 Linear—The linear trend line is a bestfit straight line that is used to estimate simple linear relationships. A linear trend highlights a rate of change that is increasing or decreasing at a steady rate. The formula for the linear trend line is as follows:
 y = the pixel's variable value.
 x = the dimension value.
 ß0 = the yintercept.
 ß1 = the linear slope or rate of change.
ß1 > 0 indicates an increasing trend.
ß1 < 0 indicates a decreasing trend.
 Polynomial—The polynomial trend line is a curved line that is useful for data that fluctuates. In this case, a polynomial order value is used to indicate the maximum number of fluctuations that occur. The formula for the polynomial trend line is as follows:
 y = the pixel's variable value
 x = the dimension value
 ß0, ß1, ß2, ß3, ..., ßn = constant coefficients
 Harmonic—The harmonic trend line is a periodically repeating curved line that is best used to describe data that follows a cyclical pattern, such as seasonal temperature changes. The formula for the harmonic trend line is as follows:
 y = the pixel's variable value
 t = the Julian date
 ß0 = the yintercept
 ß1 = the rate of change
 α, γ = coefficients of interannual or intraannual changes
 ω = i
 f = the harmonic frequency