# Probabilistic Forecasting

Toto is a foundational model designed for multivariate time series forecasting, particularly optimized for efficient processing of observability metrics

Chronos is a family of pre-trained time series forecasting models based on language model architecture, which transforms time series into token sequences for training through quantization and scaling.

Chronos is a family of pretrained time series forecasting models based on language model architecture, which supports probabilistic forecasting by converting time series into token sequences for training.

Chronos-T5 is a pre-trained time series forecasting model based on a language model architecture. It converts time series into token sequences through quantization and scaling for training, making it suitable for various time series forecasting tasks.

Chronos is a family of pretrained time series forecasting models based on language model architectures, trained by quantizing and scaling time series into token sequences.

Chronos is a family of pretrained time series forecasting models based on language model architectures, trained by converting time series into token sequences, supporting probabilistic forecasting.

Chronos is a family of pretrained time series forecasting models based on language model architectures, trained by quantizing and scaling time series into token sequences.

Chronos is a family of pre-trained time series forecasting models based on language model architecture, which transforms time series into token sequences for training to achieve probabilistic forecasting.

Chronos is a family of pre-trained time series forecasting models based on language model architectures. It converts time series into token sequences through quantization and scaling for training, suitable for probabilistic forecasting tasks.

Chronos-T5 is a pretrained time series forecasting model based on a language model architecture, achieving probabilistic forecasting by converting time series into token sequences and training the language model using cross-entropy loss.