3 ms·

So the time series are provided with no context? It's just trained on lots of sets of numbers? Then you give it a new set of numbers and it guesses the rest, ag

by dash2 6mo ago

So the time series are provided with no context? It's just trained on lots of sets of numbers? Then you give it a new set of numbers and it guesses the rest, again with no context? My guess as to how this would work: the machine will first guess from the data alone if this is one of the categories it has already seen/inferred (share prices, google trend cat searches etc.) Then it'll output a plausible completion for the category. That doesn't seem as if it will work well for any categories outside the training data. I would rather just use either a simple model (ARIMA or whatever) or a theoretically-informed model. But what do I know.

Tarq0n 6mo ago
If it works for predicting the next token in a very long stream of tokens, why not. The question is what architecture and training regimen it needs to generalize.
wtyvn 6mo ago
I think I'm in the same boat as you are, in preferring more conventional approaches to time series analysis. I'm curious as to how this would compare to having an actual statistician work on your data, because I feel that time series work is as much an art as it is a science. To start, selection of an appropriate timeframe is always important to ensure our data doesn't resemble either white noise or a random walk, and that we've given the response time of our data appropriate consideration! I find that people unfamiliar with statistics miss this point - I get people asking why I might use a weekly or biweekly timeframe for data when they reckon I should be using hourly or daily data. Selection of appropriate predictors is also important for multivariate time series and I have no idea how this model approaches that. I also have questions about how interpretable the results outputted by this model are. With a more "traditional" model, I can easily look at polyroot or the [P/E]ACF, as well as various other diagnostic tools, and select a relatively simple model that results in a decent 95% prediction interval. I've always been very wary of black box models simply because I wouldn't be able to explain any findings derived from them well. From skimming the blog post, is MAE all they're using for measuring the output quality?

by dash2 6mo ago

Tarq0n 6mo ago

If it works for predicting the next token in a very long stream of tokens, why not. The question is what architecture and training regimen it needs to generalize.

wtyvn 6mo ago

I think I'm in the same boat as you are, in preferring more conventional approaches to time series analysis. I'm curious as to how this would compare to having an actual statistician work on your data, because I feel that time series work is as much an art as it is a science. To start, selection of an appropriate timeframe is always important to ensure our data doesn't resemble either white noise or a random walk, and that we've given the response time of our data appropriate consideration! I find that people unfamiliar with statistics miss this point - I get people asking why I might use a weekly or biweekly timeframe for data when they reckon I should be using hourly or daily data. Selection of appropriate predictors is also important for multivariate time series and I have no idea how this model approaches that. I also have questions about how interpretable the results outputted by this model are. With a more "traditional" model, I can easily look at polyroot or the [P/E]ACF, as well as various other diagnostic tools, and select a relatively simple model that results in a decent 95% prediction interval. I've always been very wary of black box models simply because I wouldn't be able to explain any findings derived from them well. From skimming the blog post, is MAE all they're using for measuring the output quality?