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 are available, including simple curve fitting, exponential smoothing, and a forest-based method.
| Tool | Description |
|---|---|
Forecasts the values of each location of a space-time cube using curve fitting. | |
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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. | |
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Forecasts the 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 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. |
Additional resources
https://www.esriurl.com/spatialstats contains a list of resources to help you use the Spatial Statistics tools, including the following:
- Tutorials
- Videos
- Free web seminars
- Books, articles, and white papers
- Sample scripts, case studies, and Learn Lessons