mlforecast
Scalable machine 🤖 learning for time series forecasting. (by Nixtla)
hierarchicalforecast
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods. (by Nixtla)
| mlforecast | hierarchicalforecast | |
|---|---|---|
| 11 | 11 | |
| 732 | 526 | |
| 5.3% | 4.0% | |
| 8.7 | 6.7 | |
| 11 days ago | about 1 month 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.
mlforecast
Posts with mentions or reviews of mlforecast.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-25.
-
Sales forecast for next two years
MLForecast
-
Demand Planning
Alternatively you could try out their mlforecast package which 'featurizes' the time pieces to fit with things like LightGBM: https://github.com/Nixtla/mlforecast
-
Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/mlforecast/
-
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
-
Time series modeling using ARIMA and XGBoost. Intro to free time series modeling resources
In Python you can use the https://nixtla.github.io/mlforecast library for example, it makes the feature engineering, evaluation and cross validation trivial.
-
Time series forecasting model predicts increasing number for target variable when the actual values are zeroes
You might want to take a look to this library: MLForecast.
-
[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
Yes, for example we have this paper in long-horizon settings using our library NeuralForecast and this experiment with other of our libraries MLForecast, both of them outperforming autoarima.
-
[P] Deep Learning for time series forecasting (neuralforecast, python package)
We are already working on the comparison. For the moment, the blog shows that another of our libraries, MLForecast (https://github.com/Nixtla/mlforecast), has an excellent performance in this use case.
-
Automated Time Series Processing and Forecasting
We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library (https://github.com/nixtla/mlforecast) that builds upong Sckilearn .xgboos and lightbmg .
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.
-
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
-
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
-
[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.
-
[D] Python's library to multivariate time series forecasting: Sktime, modeltime, darts.
Here is the repo for hierarchical methods: https://github.com/nixtla/hierarchicalforecast/
-
Time series forecasting model predicts increasing number for target variable when the actual values are zeroes
You can try HierarchicalForecast package to reconciliate predictions.
-
[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 mlforecast and hierarchicalforecast you can also consider the following projects:
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
hts - Hierarchical and Grouped Time Series
pytorch-forecasting - Time series forecasting with PyTorch
atspy - AtsPy: Automated Time Series Models in Python (by @firmai)
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
dicomtrolley - Retrieve medical images via WADO, MINT, RAD69 and DICOM-QR
darts - A python library for user-friendly forecasting and anomaly detection on time series.
recon-cli - Simple command line tool to reconcile datasets
tsai - Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
nixtla - TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
mlforecast vs statsforecast
hierarchicalforecast vs statsforecast
mlforecast vs tsfeatures
hierarchicalforecast vs hts
mlforecast vs pytorch-forecasting
hierarchicalforecast vs atspy
mlforecast vs neuralforecast
hierarchicalforecast vs dicomtrolley
mlforecast vs darts
hierarchicalforecast vs recon-cli
mlforecast vs tsai
mlforecast vs nixtla