Covariates

Covariates are additional variables that can improve forecast accuracy. They come in two forms:

User-provided covariates

Pass these directly in the series objects:

• hist_variables — variables with values only for the historical period (same length as target). Example: recorded temperature.
• future_variables — variables with values for both history and forecast period. Length = history + horizon. Example: planned promotions, weather forecasts.
• static_variables — variables that don't change over time. Single numeric value per variable. Example: population, store size.

TFC-provided covariates

Use the covariates array in the request body to automatically add covariates managed by TFC:

Holidays

Code
{
  "type": "holidays",
  "config": {
    "country": "US",
    "smoothing_method": "auto",
    "window_size": 15,
    "sigma": 7.0,
    "ramp_days": 14
  }
}

• country (required): 2-character ISO country code (e.g., US, GB, FR, DE)
• smoothing_method: auto (default), none, gaussian, exponential, ramp, flat
• window_size: 1–15 (default 15), should be odd
• sigma: standard deviation for Gaussian kernel (default 7.0)
• ramp_days: days before/after for ramp (1–35, default 14)

Events

Code
{
  "type": "events",
  "config": {
    "country": "US"
  }
}

• country (required): ISO country code or "Global"

Temporal features

Code
{
  "type": "temporal_features",
  "config": {}
}

Automatically adds time-based features (day of week, month, etc.).