21–30 of 44 posts
A minor language error: "this model does not outperform classical statistical methods neither in accuracy nor speed." should say "either" and "or".
https://dictionary.cambridge.org/grammar/british-grammar/nei...
Earlier quoted context omitted.
While the results don't prove the superiority convincingly, it does seem that ETS is a good candidate as a first go-to in practical applications. "In practice, practice and theory are the same. In theory, they are not.”
In theory practice and theory are the same. In practice they are not.
Earlier quoted context omitted.
It's true simply because large ANNs have a higher capacity, which is great for large, nonlinear data but less so for small datasets or simple functions. In any case, Transformers are eating ML right now and I'm actually surprised there's no "GPT-3 for time series" yet. It's technically the same problem as language modeling (that is, multi-step prediction of numerics), however, there is only a comparably little amount…
> 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.) Ti…
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.
In the original Prophet paper ( https://peerj.com/preprints/3190.pdf ) they claim that Prophet outperforms ETS (see Figure 7, for example). And in the NeuralProphet paper, they claim that it outperforms Prophet (but do not, as far as I can see, compare directly to ETS). Here we see ETS outperforms NeuralProphet. Presumably this apparent non-transitivity is because of differences in each evaluation. If we fix the eval…
Can someone explain this? I don't know what the context is for this Show HN.
We noted that the paper only compares NeuralProphet against Prophet and does not include standard time series datasets (such as M-competitions). So we decided to test the model against simpler models (ETS in this case) using the StatsForecast library (https://github.com/Nixtla/statsforecast/).
Hmm, wow. When I saw the headline, I assumed they used like one dataset or something similarly limiting. I'd need to dig out the original paper, but I would be surprised if the original didn't compare to basic benchmark methods. But from memory, I never saw such a comparison (until now).
This wouldn't pass peer-review of it were a paper. Major issues: - No fair hyperparametrization for Neural Prophet. They mention multiple times they used default hyperparams or ad-hoc example hyperparams. - 3/4 benchmark datasets (one they didn't finish training) where ETS outperforms is not strong evidence of all-round robustness. Benchmarks like SuperGlue for NLP combine 10 completely different tasks with more subt…
Regarding the hyperparameter selection, we went beyond the original NeuralProphet paper, tunning and actively trying to help improve its performance. We even made a PR fixing NP bugs in the process.
In the original Prophet paper ( https://peerj.com/preprints/3190.pdf ) they claim that Prophet outperforms ETS (see Figure 7, for example). And in the NeuralProphet paper, they claim that it outperforms Prophet (but do not, as far as I can see, compare directly to ETS). Here we see ETS outperforms NeuralProphet. Presumably this apparent non-transitivity is because of differences in each evaluation. If we fix the eval…
In recent years, FB-Prophet has gained a reputation for the poor quality of its predictions in many practical scenarios (short/medium term horizon) and its slow performance on bigger data sets; our ARIMA/ETS work and NeuralProphet confirmed those suspicions. The mentioned 92 percent improvements of the paper are restricted to h in {1,3,15,60} (https://arxiv.org/pdf/2111.15397.pdf).
This post's results are rather for short-horizon tasks, the same as NeuralProphet experiments. But we are confident that specialized tools like N-HiTS would outperform Prophet in long-horizon settings.