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Pick a forecasting model and train it to predict future values of a numeric column.


The speed and quality slider is more of a spectrum. This setting determines the number of machine learning models considered. Models that do not support the training data are automatically excluded.

When you run the Forecast tool, you make a model capable of in-sample predictions. Symon.AI then creates predictions for the dates in the available data. Finally, using both the actual and the predicted data, Symon.AI computes the error score. For more information about error scores, read: Understanding scores.

Forecasting models

  • Automatic: Picks the best available forecasting model. It trains the model to predict future values of a numeric column.

  • ARIMA: Short for "Auto Regressive Integrated Moving Average". It applies Auto Regressive and Moving Average models to time series data to predict the future.

  • Linear Regressive: Identifies underlying trends. It draws a straight line through data points to minimize the distances between data points and the resulting trend line.

  • Prophet: Forecasts time series data by incorporating non-linear trends, weekly, daily seasonality, and holiday effects into its additive model. Use this for time series with strong seasonal effects and multiple seasons of historical data.

  • Seasonal ARIMA: An extension of ARIMA. It supports univariate time series data with a seasonal component.

  • Exp Smoothing: Assigns greater weighting to more recent observations. This smoothes out time series data using the exponential window function to make predictions.

  • Simple Exp Smoothing:The simplest of the exponentially smoothing methods. Use this to forecast data with no clear trend or seasonal pattern.

Input and output

To use this tool, you must include:

  • A date column.

  • A value column.

  • A frequency, which refers to the frequency of the time periods used in the date column.

The results will forecast for that number of periods according to the frequency you select.

The forecast tool outputs numeric values.

Analyze by

Optionally, fill in the Analyze by fields for more control over how Symon.AI forecasts your data. When you select a column to analyze by, Symon.AI might make different predictions for each group. 

For example, let's say you have a data set with 4 columns: seller ID, pay component, payout, date. If you want to forecast a seller's payout, it would be useful to analyze this by pay component. Symon.AI takes this into account and gives more nuanced results that take pay component over time into consideration. This can also help give better results if the number of rows differs between pay components.


You can view the data collected by these forecasting models in the Forecasting Chart, a visual representation of your forecasting data.