chronos-forecasting

And https://github.com/amazon-science/chronos-forecasting

On a related note, Amazon also had a model for time series forecasting called Chronos. https://github.com/amazon-science/chronos-forecasting

There were some developments using LLMs in the timeseries domain which caught my attention. I toyed with the Chronos forecasting toolkit [1], and the results were predictably off by wild margins [2] What really caught my eye though was the "feel" of the predicted timeseries -- this is the first time I've seen synthetic timeseries that look like the real thing. Stock charts have a certain quality to them, once you've been looking at them long enough, you can tell more often than not whether some unlabeled data is a stock price timeseries or not. It seems the chronos LLM was able to pick up on that "nature" of the price movement, and replicate it in its forecasts. Impressive! 1: https://github.com/amazon-science/chronos-forecasting 2: https://imgur.com/a/hTRQ38d

Yes there are many attempts in applying a transformers to timeseries forecasting. For instance (but there are many more): - Timegpt https://arxiv.org/abs/2310.03589 - Chronos https://github.com/amazon-science/chronos-forecasting These kind of papers often talk the world, but often lack a proper baseline model. They only compare against very simple (naive forecast), or non tuned models. In my experience a gradient boosting model will probably solve 95% of your forecasting problems, and trying to get fancy with a transformer (or even just a simple neural net) is more trouble then it is worth.

Code: https://github.com/amazon-science/chronos-forecasting