atspy
hierarchicalforecast
| atspy | hierarchicalforecast | |
|---|---|---|
| 1 | 11 | |
| 506 | 517 | |
| - | 2.3% | |
| 0.0 | 6.7 | |
| over 1 year ago | 16 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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atspy
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).
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
Discounted-Udemy-Course-Enroller - A script/software for automatically enrolling/joining 100% discounted Udemy courses for free. Get Paid Udemy courses for free with just a few clicks.
hts - Hierarchical and Grouped Time Series
tslearn - The machine learning toolkit for time series analysis in Python
dicomtrolley - Retrieve medical images via WADO, MINT, RAD69 and DICOM-QR
neural_prophet - NeuralProphet: A simple forecasting package
recon-cli - Simple command line tool to reconcile datasets
sktime - A unified framework for machine learning with time series
pmdarima - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
saved-reddit-posts - A Python script to automate saving posts on Reddit as PDFs