NTNk.jl
Unsupervised Machine Learning: Nonnegative Tensor Networks + k-means clustering (by TensorDecompositions)
hierarchicalforecast
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods. (by Nixtla)
| NTNk.jl | hierarchicalforecast | |
|---|---|---|
| 1 | 11 | |
| 8 | 522 | |
| - | 3.3% | |
| 1.8 | 6.7 | |
| almost 3 years ago | 18 days ago | |
| Julia | Python | |
| GNU General Public License v3.0 only | 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.
NTNk.jl
Posts with mentions or reviews of NTNk.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-15.
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Tensor decompositions in Julia?
From my dear friend google: TensorToolbox.jl. How is that? There's also NTNk.jl. I haven't used either of these.
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).
What are some alternatives?
When comparing NTNk.jl and hierarchicalforecast you can also consider the following projects:
tsfresh - Automatic extraction of relevant features from time series:
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
ITensors.jl - A Julia library for efficient tensor computations and tensor network calculations
hts - Hierarchical and Grouped Time Series
TensorToolbox.jl - Julia package for tensors as multidimensional arrays, with functionalty within Tucker format, Kruskal (CP) format, Hierarchical Tucker format and Tensor Train format.
atspy - AtsPy: Automated Time Series Models in Python (by @firmai)
dicomtrolley - Retrieve medical images via WADO, MINT, RAD69 and DICOM-QR
recon-cli - Simple command line tool to reconcile datasets