Time series forecasting is using a model to predict future values based on previously observed values.In a time series forecast, the prediction is based on history and we are assuming the future will resemble the past. We project current trends using existing data.

The following is a high-level description of the two pre-packaged time series methodologies our predictive tools support:

• ARIMA Model to TS Forecast

ARIMA is the Autoregressive Integrated Moving Averageandis the most commonly used forecasting approach

• ETS Model to TS Forecast

ETSmodel uses exponential smoothing method andis a commonly used forecasting approach based on a weighted average of past observations with the weights declining in size for more distant past values. In short, all past values are factored in the forecast although with decreasing importance as the values are further back in time.

Additionally the video walks you through how to confirm the completeness of your data using the Field Summary tool and shows you how to use TS Compare the two analyze the error levels of the forecasting methods.

Time Series is comprised of a variety of tools within Alteryx, which are part of the standard Alteryx Designer License. Alteryx customers use predictive analytics to identify patterns found in historical and transactional data to identify risks as well as opportunities. Alteryx Predictive analytic tools are built on Open source R. Alteryx users are not required to know R to execute predictive models because all of the models in Alteryx are packaged into easy-to-use macro tools that only require configuration. All predictive tools are macros, and therefore not a “black box”. Macros provide the user with the flexibility to open all models and dissect the logic, as well as see and modify the R-script(s) being executed.

This video provides a brief tutorial of using Times Series tools on historical single family home sales and includes an overview on how to configure the following tools:
Field Summary, ARIMA, ETS, TS Compare, and TS Forecast