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.
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.
Further information on DARR mode can be found in this published paper, Memory-Augmented Forecasting: Scalability and Generalization Across Temporal Domains
Kumo-Forecast combines four main components: a pretrained temporal encoder, a trainable forecasting head, historical-pattern retrieval mechanism and an interpretability framework.
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