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Somehow I missed that one. Are there any competition on this? I always had difficulties with ML and time series, I'll need to try that out.
https://moment-timeseries-foundation-model.github.io/
https://arxiv.org/abs/2403.07815
A friend at work used one to predict when our CEO would post in Slack, which is verry entertaining to see if correct.
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…
- decomposition: discover a more general form of Fourrier transform to untangle the underlying factors
- memorization: some patterns are recurrent in many domains such as power low
- multitask: exploit cross-domain connections such as weather vs electricity
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…
What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).
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…
What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).
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…
What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).
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…
As they say in appendix 8:
> We create the synthetic data to reflect common time-series patterns using traditional statistical models. We start with four simple times series patterns:
> • Piece-wise linear trends (I), where the number of the piece-wise linear components is randomly chosen between 2 and 8.
> • ARMA(p, q) (II), where 1 ≤ p, q ≤ 8 and the corresponding coefficients are generated from either a multivariate Gaussian or a uniform, then normalized.
> • Seasonal patterns. In particular we create the sine (III) and the cosine (IV) waves of different random periods between 4 and max context length / 2 time-points and time delays.
If there were no such underlying patterns in the class of all time-series data, then even the idea of traditional time-series models would be fundamentally misplaced.
And since this is a transformer model, it also looks for patterns in the problem-specific input data at inference time, just like how the input context to an LLM influences its output's relevance.
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…
What is not generally understood is that these models don’t predict egg prices or inflation in Italy. They decompose a time series into trends, seasonality and residuals. That’s what they are actually modelling. They cannot predict wars in the Middle East influencing inflation unless there is a seasonal pattern(s).
There is infinitely more entropy in the real world out there than any model can even remotely capture.
The world is not minecraft.