6 ms·
Exponential Smoothing: faster and more accurate than NeuralProphet
We benchmarked on more than 55K series and show that ETS improves MAPE and sMAPE forecast accuracy by 32% and 19%, respectively, with 104x less computational time over NeuralProphet.
We hope this exercise helps the forecast community avoid adopting yet another overpromising and unproven forecasting method.
- rich_sasha 4y agoHmm, 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).
- maxmc 4y agoBe surprised. Here is the original Neuralprophet paper: https://arxiv.org/pdf/2111.15397.pdf https://arxiv.org/pdf/2111.15397.pdf It only compares itself to Prophet.
- ren_engineer 4y agowhat's the consensus on machine learning vs more classical methods for time series forecasting? I know in 2018 a hybrid model won the M4 competition, obviously in this case classical still beats AI/ML https://en.wikipedia.org/wiki/Makridakis_Competitions https://en.wikipedia.org/wiki/Makridakis_Competitions
- dcastm 4y agoIn the M5 competition[1], most winning solutions used LightGBM. So ML beat classical. Just a couple of the winning solutions used DL. [1] https://www.sciencedirect.com/science/article/pii/S0169207021001874 https://www.sciencedirect.com/science/article/pii/S016920702...
- ultrabear 4y agoI would like to see the results of this ETS on the M5 Competition dataset, and see how fast it is compared to the ETS that was used as a benchmark. It goes without saying that accuracy is important, but reducing the total execution time is also pretty valuable.
- rich_sasha 4y agoI think depends massively in what you mean by "time series". If it is really an ARMA model you're looking at then ML can only bring noise to the problem. If it is a complex large system that happens to be indexed by time, ML can well be better. AFAIK Prophet had more modest scope than "be all and end all of TS modelling", rather a decent model for everything. It might indeed be excellent at that...
- beernet 4y agoAs usual in ML, the appropriate solution depends on the problem and context. ML (particularly DL) tends to outperform "classical" statistical time series forecasting when the data is (strongly) nonlinear, highly dimensional and large. The opposite holds as well. It is also important to note that accuracy is not the only relevant metric in practical applications. Explainability is of particular interest in time series forecasting: it is good to know if your sales are going to increase/decrease, but it is even more valuable to know which input variables are likely to account for that change. Hence, a "simple" model with inferior forecasting accuracy might be preferred to a stronger estimator if it can give insights to not only the "what" will happen, but also the "why".
- tomwphillips 4y ago> ML (particularly DL) tends to outperform "classical" statistical time series forecasting when the data is (strongly) nonlinear, highly dimensional and large. This claim about forecasting with DL comes up a lot, but I’ve seen little evidence to back it up. Personally, I’ve never managed to have the same success others apparently have with DL time series forecasting.
- beernet 4y agoIt'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 of human-generated data for self-supervised learning of a time series forecasting model. Another reason might be that the expected applications and potentials of such a pre-trained model aren't as glamorous as generating language.
- time_to_smile 4y ago> 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.
- mkl 4y agoA minor language error: "this model does not outperform classical statistical methods neither in accuracy nor speed." should say "either" and "or".
- ISV_Damocles 4y agohttps://dictionary.cambridge.org/grammar/british-grammar/neither-neither-nor-and-not-either_2 https://dictionary.cambridge.org/grammar/british-grammar/nei...
- mkl 4y agoNothing there seems to contradict me. The problem in the linked page is that "neither ... nor" is used after "not", which makes it a double negative.
- lightedman 4y agoThe "Not-Neither-Nor" sequence is typical, even with regards to American English, versus British English (the Queen's English.) In either case, both are technically-correct.
- bo1024 4y agoAs part of a double negative?
- kevin_thibedeau 4y agoEnglish is nothing if not inconsistent.
- Imnimo 4y agoIn the original Prophet paper (https://peerj.com/preprints/3190.pdf 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 evaluation to the method used here, is it still the case that NeuralProphet outperforms Prophet (and therefore the claim that Prophet outperforms ETS is not correct)? Or is it that NeuralProphet does not outperform Prophet, but Prophet does outperform ETS?
- fedegr 4y agoI think the problem arises from the datasets used to evaluate the performance of the models. In the case of Prophet's paper, only one time series is used (The number of events created on Facebook). We can conclude from the results comparing AutoARIMA vs. Prophet (https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter https://github.com/Nixtla/statsforecast/tree/main/experiment..., using the same datasets as in the ETS vs. NeuralProphet experiment) that ETS is also better than Prophet. Regarding NeuralProphet vs. Prophet, the results are not conclusive for these datasets.
- maxmc 4y agoFigure 7 of the mentioned paper evaluates FB-Prophet in a extremely convenient environment of long horizon h in {30,60,90,120,150,180}. It is known that ETS and ARIMA models concatenate errors and degrade in performance with longer forecasting horizons. We have explored and offered solutions to these issues with the N-HiTS model specialized in long-horizon (https://arxiv.org/abs/2201.12886 https://arxiv.org/abs/2201.12886). 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 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.
- cercatrova 4y agoCan someone explain this? I don't know what the context is for this Show HN.
- IshKebab 4y agoThey're time series prediction methods. E.g. they mention electricity usage forecasting - given historical data, what will the usage be in 1 hour? Facebook's Prophet is quite popular in the space I understand. No idea about the other two.
- fedegr 4y agoNeuralProphet is the successor-extension of Prophet, and it aims to provide Prophet with neural networks and autoregressive terms. The paper can be found here (https://arxiv.org/abs/2111.15397?fbclid=IwAR2vCkHYiy5yuPPjWXpJgAJs-uD5NkH4liORt1ch4a6X_kmpMqagGtXyez4 https://arxiv.org/abs/2111.15397?fbclid=IwAR2vCkHYiy5yuPPjWX...). 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/ https://github.com/Nixtla/statsforecast/).
- gillesjacobs 4y agoThis 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 subtasks to assess language model performance. And even SuperGlue is not uncontroversial.
- gillesjacobs 4y agoWhile 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.”
- variaga 4y agoIn theory practice and theory are the same. In practice they are not.
- gillesjacobs 4y agoHN pedantry ruins the fun of wordplay yet again.
- anon_123g987 4y agoIn theory, his version is right. In practice, yours.
- lr1970 4y agoThe difference between theory and practice is greater in theory than in practice.
- timy2shoes 4y agoIn theory there is no difference between theory and practice. In practice there is. - Yogi Berra
- tmaly 4y agoI wish the introduction was targeted to a more general audience. It was not very clear what the application was.
- jenkstom 4y agoThis makes me reconsider my opposition to a death penalty.