hierarchicalforecast
Probabilistic Hierarchical forecasting đź‘‘ with statistical and econometric methods. (by Nixtla)
statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models. (by Nixtla)
| hierarchicalforecast | statsforecast | |
|---|---|---|
| 11 | 58 | |
| 522 | 3,565 | |
| 2.3% | 2.7% | |
| 6.7 | 8.9 | |
| 17 days ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hierarchicalforecast
Posts with mentions or reviews of hierarchicalforecast.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-29.
- [D] When less is more in the hierarchical forecasting case.
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Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
- Show HN: Probabilistic hierarchical forecasting with statistical methods
- Sh: Probabilistic hierarchical forecasting with statistical methods
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Probabilistic and nonnegative methods for hierarchical forecasting in python are now available in Nixtla's HierachicalForecast
Repo: https://github.com/Nixtla/hierarchicalforecast Example: https://nixtla.github.io/hierarchicalforecast/examples/australiandomestictourism-intervals.html
- Probabilistic hierarchical reconciliation for time series
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[D] Can anyone explain the MinTrace method for reconciliation of Hierarchical Time Series Forecast?
If you use python take a look to the HierarchicalForecast package.
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[D] Python's library to multivariate time series forecasting: Sktime, modeltime, darts.
Here is the repo for hierarchical methods: https://github.com/nixtla/hierarchicalforecast/
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Time series forecasting model predicts increasing number for target variable when the actual values are zeroes
You can try HierarchicalForecast package to reconciliate predictions.
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[D] What are some statistical packages you use in R that aren't available in Python?
[HierarchicalForecast package](https://github.com/Nixtla/hierarchicalforecast) that mirrors [hts](https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf) that is now part of fable. The same with previous comment on efficient implementations of ARIMA and ETS on the [StatsForecast package](https://github.com/Nixtla/statsforecast).
statsforecast
Posts with mentions or reviews of statsforecast.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-13.
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TimeGPT-1
I 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
- Facebook Prophet: library for generating forecasts from any time series data
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Sales forecast for next two years
If you only have historical data: StatsForecast
-
Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
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Demand Planning
If you are mostly worried about time and use python you could try out Nixtla's statsforecast as it is very snappy. https://github.com/Nixtla/statsforecast
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Statistical vs Machine Learning vs Deep Learning Modeling for Time Series Forecasting
I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/statsforecast/
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XGBoost for time series
Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast
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[Discussion] Amazon's AutoML vs. open source statistical methods
In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods.
- Statistical methods outperform Amazon’s ML Forecast
What are some alternatives?
When comparing hierarchicalforecast and statsforecast you can also consider the following projects:
hts - Hierarchical and Grouped Time Series
darts - A python library for user-friendly forecasting and anomaly detection on time series.
atspy - AtsPy: Automated Time Series Models in Python (by @firmai)
mlforecast - Scalable machine 🤖 learning for time series forecasting.
dicomtrolley - Retrieve medical images via WADO, MINT, RAD69 and DICOM-QR
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
recon-cli - Simple command line tool to reconcile datasets
nixtla - Python SDK for TimeGPT, a foundational time series model
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
fable - Tidy time series forecasting
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
pytorch-forecasting - Time series forecasting with PyTorch
hierarchicalforecast vs hts
statsforecast vs darts
hierarchicalforecast vs atspy
statsforecast vs mlforecast
hierarchicalforecast vs dicomtrolley
statsforecast vs neuralforecast
hierarchicalforecast vs recon-cli
statsforecast vs nixtla
statsforecast vs tsai
statsforecast vs fable
statsforecast vs flow-forecast
statsforecast vs pytorch-forecasting