Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.
Enables users to perform deterministic time series forecasting through Claude by running reproducible Python statistical and foundation models, providing tools for loading data, cross-validation, forecasting, anomaly detection, and exporting re-runnable manifests without any LLM involvement.
Enables demand forecasting and replenishment recommendations using statistical models (Syntetos-Boylan classification, AutoETS, TSB) and provides tools for forecasting, evaluation, and order quantity calculation.
Enables multitenant time series forecasting and anomaly detection using Nixtla's TimeGPT, with support for fine-tuning, rolling backtests, and usage tracking.