NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability

Highlights (TL;DR)

NVIDIA Kumo Forecast (formerly known as NV-Tesseract Forecasting) is a multi-variate time-series forecasting model that combines a pretrained Transformer backbone with Domain-Aware Representation and Retrieval (DARR), a context-enhanced forecasting mode that blends direct model predictions with patterns retrieved from historical data. This includes interpretability of the results. Built on the MOMENT-1-large backbone, Kumo Forecast uses an approximately 340-million-parameter base encoder plus a trainable forecasting head.

The library provides a DataFrame-first API, configurable forecast horizons, optional historical context for retrieval, and an interpretability framework that produces horizon-specific explanations without modifying the underlying forecaster.

Kumo-Forecast is available under Apache 2.0 license.

Model code: NVIDIA/Kumo-TS
Model weights: nvidia/Kumo-Forecast

The Shift to Context-Enhanced Time-Series Forecasting

A forecasting system can draw on two different sources of information: representations learned during pretraining and historical observations available for the current application. Kumo-Forecast brings these together through a pretrained forecasting model and an optional retrieval pathway over historical context.

In its direct forecasting path, Kumo-Forecast uses a MOMENT-based Transformer and a trained forecasting head to predict future values. In Domain Aware Representation and Retrieval (DARR) mode, it also retrieves related patterns from historical data using a nearest-neighbor approach and combines that information with the direct forecast.

This creates a distinction between the model’s input window and its retrieval context. The input window supplies recent observations for the forecast; a separate historical DataFrame can supply additional context for pattern retrieval. Developers can adjust how strongly the final prediction relies on each pathway through the library’s configuration options.