The tools in the Time Series Forecasting toolset allow you to forecast and estimate future values at locations in a space-time cube as well as evaluate and compare forecast models for each location. 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 the random forest algorithm, which is a supervised machine learning method developed by Leo Breiman and Adele Cutler. The forest regression model is trained using time windows on each location of the space-time cube. |
Additional resources
The Spatial Statistics Resources page contains a variety of resources to help you use the Spatial Statistics and Space Time Pattern Mining tools, including the following:
- Hands-on tutorials
- Workshop videos and presentations
- Training and web seminars
- Links to books, articles, and technical papers
- Sample scripts and case studies