Time-series forecasting

Key Features

• •Models temporal dependence and seasonality (e.g., daily, weekly, yearly patterns)
• •Supports a range of methods from classical statistics (ARIMA, ETS) to modern ML and deep learning (gradient boosting, RNNs, Transformers)
• •Handles multivariate inputs, exogenous variables, and hierarchical time series
• •Can incorporate uncertainty estimates and prediction intervals, not just point forecasts
• •Often includes automatic model selection, hyperparameter tuning, and feature engineering for lags and calendar effects

Pricing

Time-series forecasting is a methodological domain rather than a product; many open-source libraries (e.g., statsmodels, Prophet, GluonTS, darts) and commercial platforms (cloud ML services, forecasting SaaS) implement these techniques with their own pricing models.

Alternatives

Links