tsai
nixtla
| tsai | nixtla | |
|---|---|---|
| 4 | 8 | |
| 4,690 | 1,429 | |
| 2.2% | 8.9% | |
| 7.4 | 9.5 | |
| 8 days ago | 4 days ago | |
| Jupyter Notebook | 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.
tsai
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Aeon: A unified framework for machine learning with time series
Also https://github.com/timeseriesAI/tsai
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What is the current state-of-art in sequence classification?
You might be interested in tsai. I am not affiliated with them and have not used tsai, but I have been planning to try it for too long … well :p
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
how about tsai?
- Machine learning with Time series data
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?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
sktime-dl - DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
pytorch-forecasting - Time series forecasting with PyTorch
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
nixtlats - Deep Learning for Time Series Forecasting.