The horizon parameter in time series forecasting specifies how far into the future the model should generate predictions. It is expressed in the same units as the time series frequency (e.g., hours, days, weeks, months).

• Incorrect Options:
  • A. The time column for forecasting: This is specified using the time_col parameter, indicating the column containing timestamps in the data.
  • B. The frequency of the time series: This is determined by the time interval between observations in the data, often specified using a different parameter like frequency.
  • D. The maximum time to wait for AutoML trials: This is typically controlled by a parameter like timeout or max_experiment_duration.

Example:

• With daily sales data and horizon = 7, the model forecasts sales for the next 7 days.
• With hourly traffic data and horizon = 24, the model forecasts traffic for the next 24 hours.

Key Points:

• Setting an appropriate horizon aligns forecasts with your application's needs.
• Longer horizons generally lead to less accurate predictions due to increased uncertainty further into the future.
• AutoML experiments with different forecasting models to find the best fit for your data and horizon requirements.