KLklaussilveira·Mar 20, 2025Submission
Statsforecast: Fast Python forecasting with statistical and econometric models
3 points · Open discussionNixtla
statsforecast
Python2021–2025
Nixtla / statsforecast
statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.
7Distinct people
15Mentions
0Explicit recommenders
5KGitHub stars
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Lightning ⚡️ fast forecasting with statistical and econometric models.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
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HN mentions by year15 dated mentions
Observed mentions: 2021-12-06 16:11:22 UTC to 2025-03-20 15:38:23 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 10 | 8 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
15 sourcesI can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
In TimeGPT-1Not really sure what you are looking for, but the easiest might be to just add lags of your input variables in the same linear model that you are using.
If you are looking for an actual timeseries method I would checkout either darts [0] or statsforecast [1]. They are currently the most mature timeseries packages.
[0] https://unit8co.github.io/darts/
[1] https://github.com/Nixtla/statsforecast
In Prophet: Automatic Forecasting ProcedureStatistical methods outperform BigQuery's Auto ML
4 points · Open discussionStatistical methods outperform Amazon’s ML Forecast
3 points · Open discussionStatistical vs. Deep Learning forecasting methods
222 points · Open discussionSimple Seasonal Naive: faster and more accurate than Prophet and NeuralProphet
1 points · Open discussionComplex Exponential Smoothing (CES) is now available in Pyhton
2 points · Open discussionStatistical Forecast: lightning fast statistical and econometric forecasting
1 points · Open discussionNeuralProphet 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=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/).
In Exponential Smoothing: faster and more accurate than NeuralProphetI 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/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.
In Exponential Smoothing: faster and more accurate than NeuralProphetExponential 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.
161 points · Open discussionExponential Smoothing (ETS) for Python
1 points · Open discussionShow HN: Fast and robust Auto Arima in Python
2 points · Open discussionThe pipeline we have developed improves the state of the art in the markets you mention in the following aspects:
1. It is a fully automated end-to-end pipeline for forecast generation. The pipeline considers preprocessing such as missing value imputation, feature generation (static and dynamic), forecast generation, and also a module to validate forecasts on important time series competition data sets.
2. Users can deploy the pipeline in their cloud quickly. We use terraform (https://github.com/Nixtla/nixtla/tree/main/iac/terraform/aws), so it is very simple to deploy the pipeline on AWS. We are working to release versions of terraform on other clouds such as Azure and Google Cloud.
3. Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On ou
Read the rest
r side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.
About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.