ARIMA Models: Traditional statistical method effective for linear trends, seasonality, short-term forecasting, interpretability, and structured time-series datasets.
SARIMA Models: Extended ARIMA approach handling seasonal patterns, making it suitable for sales, weather, and economic forecasting applications.
Facebook Prophet: User-friendly forecasting tool designed for business data, handling seasonality, holidays, missing values, and rapid deployment scenarios.
LSTM Neural Networks: Deep learning models capturing long-term dependencies, nonlinear patterns, and complex temporal relationships in large time-series datasets.
Temporal Fusion Transformers: Advanced transformer-based models offering high accuracy, interpretability, and scalability for multivariate time-series forecasting tasks.
XGBoost Regression Models: Machine learning approach leveraging engineered features, strong performance on structured data, and robustness against noisy time-series inputs.
NeuralProphet: Hybrid forecasting model combining neural networks with Prophet-style components, improving accuracy while retaining interpretability and usability.
N-BEATS Architecture: Deep learning model designed specifically for time-series forecasting, excelling in univariate scenarios with strong accuracy benchmarks.
AutoML Forecasting Systems: Automated platforms selecting optimal models, tuning parameters, reducing manual effort, and enabling scalable forecasting across industries.