The tools in the Time Series Forecasting toolset allow you to forecast and estimate future values of a space-time cube as well as evaluate and compare different forecast models at each location in a space-time cube. Various time series forecasting models can be are available, including simple curve fitting, exponential smoothing, and a forest-based method.
Forecasts the future values of each location of a space-time cube using curve fitting.
Selects the most accurate among multiple forecasting results for each location of a space-time cube. This allows you to use multiple tools in the Time Series Forecasting toolset with the same time series data and select the best forecast for each location.
Forecasts the future values of each location of a space-time cube using the Holt-Winters exponential smoothing method by decomposing the time series at each location cube into seasonal and trend components
Forecasts the future 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.
https://www.esriurl.com/spatialstats contains a list of resources to help you use the Spatial Statistics tools, including the following:
- Free web seminars
- Books, articles, and white papers
- Sample scripts, case studies, and Learn Lessons