> It's technically the same problem as language modeling
You're thinking of modeling event sequences which is not strictly speaking the same as time series modeling.
Plenty of people do use LSTMs to model event sequences, using the hidden state of the model as a vector representation of processes current location walking a graph (i.e. a Users journey through a mobile app, or navigating following links on the web.)
Time series is different because the ticks of timed events are at consistent intervals and are also part of the problem being modeled. In general time series models have often been distinct from sequence models.
The reason there's no GPT-3 for any general sequence is the lack of data. Typically the vocabulary of events is much smaller than natural languages and the corpus of sequences much smaller.
There's a deeper issue. All language (and code and other things in the GPT/etc corpora) seem to have something in common - hierarchical, short- and long-range structure.
In contrast, there is nothing that all time series have in common. There's no way to learn generic time series knowledge that will reliably generalise to new unseen time series.
You're thinking of modeling event sequences which is not strictly speaking the same as time series modeling.
Plenty of people do use LSTMs to model event sequences, using the hidden state of the model as a vector representation of processes current location walking a graph (i.e. a Users journey through a mobile app, or navigating following links on the web.)
Time series is different because the ticks of timed events are at consistent intervals and are also part of the problem being modeled. In general time series models have often been distinct from sequence models.
The reason there's no GPT-3 for any general sequence is the lack of data. Typically the vocabulary of events is much smaller than natural languages and the corpus of sequences much smaller.