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. | |

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 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