An overview of the Time Series Forecasting toolset

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
Curve Fit Forecast	Forecasts the values of each location of a space-time cube using curve fitting.
Evaluate Forecasts by Location	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.
Exponential Smoothing Forecast	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.
Forest-based Forecast	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