TBATS_FORECAST

TBATS is an advanced time series forecasting method designed to handle:

✅ Multiple seasonalities (e.g., daily, weekly, yearly)

✅ Non-stationary trends (gradual shifts over time)

✅ Box-Cox transformations (to stabilize variance)

✅ Autoregressive components (short-term dependencies)

✅ Exponential smoothing (ETS) (trend & seasonality modeling)

TBATS is particularly useful for time series data with complex and irregular seasonal patterns, such as sales with daily & weekly trends

TBATS stands for:

• T: Trigonometric seasonal components (for complex seasonality)
• B: Box-Cox transformation (for variance stabilization)
• A: ARMA errors (to capture short-term autocorrelation)
• T: Trend modeling (additive or damped)
• S: Seasonal components (multiple periodicities)

TBATS automatically detects multiple seasonal patterns and selects the best model.