I somehow find the concept of a general time series model strange. How can the same model predict egg prices in Italy, and global inflation in a reliable way?
And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
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.
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?
Let's say I have long time series of past solar irradiation and long time series of past weather forecasts. Can this model make use of weather forecasts for time X in the future to predict electricity prices in the future?
That is, can it use one time series at time X to predict another time series at time X?
Or is this strictly about finding patterns WITHIN a time series.
I'm willing to bet an intelligent LLM with a dataset and a pandas stats package could outperform this model by running its own experiments and making predictions
Instead of willing to bet, you can do it yourself and prove it. It is not like there is a ceiling for doing what you are proposing.
I am willing to bet that you are wrong.
Same with all tech scams, Even if you magically assume that they could solve their problem with this tech why on earth would they give it to the public, for free or for a price. Alphabet would just become the best quantitative hedgefund in the world.
The Cisco Time Series model is inspired by this model from Google. This one is targeted at observability data and I can confirm it works great in that context https://github.com/splunk/cisco-time-series-model
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[ 200 ms ] story [ 721 ms ] threadI always had difficulties with ML and time series, I'll need to try that out.
And how would you even use this model, given that there are no explanations that help you trust where the prediction comes from…
Easy, both go up.
There is infinitely more entropy in the real world out there than any model can even remotely capture.
The world is not minecraft.
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.
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?
That is, can it use one time series at time X to predict another time series at time X?
Or is this strictly about finding patterns WITHIN a time series.