hierarchicalforecast
ploomber
hierarchicalforecast | ploomber | |
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11 | 121 | |
522 | 3,374 | |
2.3% | 0.3% | |
6.7 | 7.4 | |
17 days ago | 24 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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hierarchicalforecast
- [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).
ploomber
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
Documentation: https://jupysql.ploomber.io
Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).
We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!
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Runme – Interactive Runbooks Built with Markdown
For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel
And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber
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Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
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Who needs MLflow when you have SQLite?
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
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New to large SW projects in Python, best practices to organize code
I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.
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A three-part series on deploying a Data Science Platform on AWS
Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
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Is Colab still the place to go?
If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!
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Alternatives to nextflow?
It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2
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Saving log files
That's what we do for lineage with https://ploomber.io/
What are some alternatives?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
hts - Hierarchical and Grouped Time Series
papermill - 📚 Parameterize, execute, and analyze notebooks
atspy - AtsPy: Automated Time Series Models in Python (by @firmai)
dagster - An orchestration platform for the development, production, and observation of data assets.
dicomtrolley - Retrieve medical images via WADO, MINT, RAD69 and DICOM-QR
dvc - 🦉 ML Experiments and Data Management with Git
recon-cli - Simple command line tool to reconcile datasets
argo - Workflow Engine for Kubernetes
MLflow - Open source platform for the machine learning lifecycle
nbdev - Create delightful software with Jupyter Notebooks