Introducing Chronos-2: From univariate to universal time series forecasting

Good news! Impressive!

How much will e.g. economics benefit from foundation models? I remember having studied econometrics a few decades ago.

"Chronos-2 can forecast single time series, multiple related time series, and time series influenced by external factors, all without needing extra training. The model uses in-context learning and a group attention feature to understand how different time series relate to each other and to factor in outside influences like weather or sales promotions.

Amazon trained Chronos-2 on synthetic data since real-world datasets with complex relationships between variables are hard to find.

Chronos-2 beat existing forecasting models by wide margins on two major benchmarks, winning over 90 percent of head-to-head comparisons against its predecessor, Chronos-Bolt. The model’s weights are now openly available, and earlier versions have been downloaded over 600 million times from Hugging Face." (Data Points newsletter)

"... time series foundation models (TSFMs) ...

Despite their success, existing TSFMs have a key limitation: they support only univariate forecasting, predicting a single time series at a time. Although univariate forecasting is important, many scenarios require additional capabilities.

Real-world forecasting problems often involve predicting multiple coevolving time series simultaneously (multivariate forecasting) or incorporating external factors that influence outcomes (covariate-informed forecasting). For example, cloud infrastructure metrics such as CPU usage, memory consumption, and storage I/O evolve together and benefit from joint modeling. Likewise, retail demand is heavily influenced by promotional activities, while energy consumption patterns are driven by weather conditions. ..."

Introducing Chronos-2: From univariate to universal forecasting - Amazon Science "In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner."

High-level depiction of Chronos. Left: Input time series is scaled and quantized to obtain a sequence of tokens. Center: The tokens are fed into a language model, which is trained using the cross-entropy loss. Right: During inference, tokens are sampled autoregressively from the model and mapped back to numerical values. (Source)