Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecastinggithub.com/amazon-science 8 pointsstellalo2 years ago3 commentsSaveHideCopy link On HNComments−huibin_shen2yOn a large scale of 42 time series datasets, Chronos demonstrates impressive empirical performance. In the zero-shot setting, it matches or even outperforms many baselines which are trained on the dataset.−sebastianpineda2ythis is an amazing contribution for the community!−cs7022yTL;DR:* Scale the time series data and quantize the floating point values into B bins.* Each bin becomes a corresponding token id in a vocabulary of B embeddings.* Train a small LLM to predict the next token id given a sequence of token ids.* At each time step, the LLM gives you a probability distribution over B bins.
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On a large scale of 42 time series datasets, Chronos demonstrates impressive empirical performance. In the zero-shot setting, it matches or even outperforms many baselines which are trained on the dataset.
this is an amazing contribution for the community!
TL;DR:
* Scale the time series data and quantize the floating point values into B bins.
* Each bin becomes a corresponding token id in a vocabulary of B embeddings.
* Train a small LLM to predict the next token id given a sequence of token ids.
* At each time step, the LLM gives you a probability distribution over B bins.