eland
nixtla
| eland | nixtla | |
|---|---|---|
| 4 | 8 | |
| 611 | 1,429 | |
| 0.8% | 8.9% | |
| 8.5 | 9.5 | |
| about 9 hours ago | 5 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
eland
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I'm getting elasticsearch.BadRequestError: BadRequestError(400, 'illegal_argument_exception', "specified fields can't be null or empty") using Eland library
We have a fix for this issue reported here merged and pending a release. Hopefully that release will happen in the next few days, then you can upgrade and the default experience for everyone won't be as confusing :)
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is it possible to use log data from elastic search and visualise it to a custom made dashboard in python?
Another option depending on what sort of data you want, and if you want to use python, is to use Eland: https://github.com/elastic/eland, together with for example Jupyter notebooks you can create super quick visualizations :)
- hey, any idea about how to automatically extract data from kibana to a panda data frame in order to be analyzed?
- Explore and analyze Elasticsearch data with Pandas-Compatible API
nixtla
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Chronos: Learning the Language of Time Series
I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...
In general, the M-Competitions (https://forecasters.org/resources/time-series-data/), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have won, generally seem to be variants of older tree-based methods (ie. LightGBM is a favorite).
Will be interesting to see whether the Transformer architecture ends up making real progress here.
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[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.
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
Here we did some comparison with prophet in the zillow real-state dataset https://github.com/Nixtla/nixtla/tree/main/utils/experiments/zillow-prophet
- Is linear regression better than prophet? Zillow benchmark
- Prophet vs. Linear Regression on Real Estate: The Zillow Case
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Automated Time Series Processing and Forecasting
Users can deploy the pipeline in their cloud quickly. We use terraform (https://github.com/Nixtla/nixtla/tree/main/iac/terraform/aws), so it is very simple to deploy the pipeline on AWS. We are working to release versions of terraform on other clouds such as Azure and Google Cloud.
What are some alternatives?
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
pandastable - Table analysis in Tkinter using pandas DataFrames.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
modin - Modin: Scale your Pandas workflows by changing a single line of code
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
pytorch-forecasting - Time series forecasting with PyTorch
kangas - 🦘 Explore multimedia datasets at scale
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
Solomon - Data Exploration tool.
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.