nixtla
TGLSTM
nixtla | TGLSTM | |
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8 | 1 | |
1,445 | 11 | |
9.9% | - | |
9.5 | 10.0 | |
3 days ago | about 4 years ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | - |
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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.
TGLSTM
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
I'm not current on what the SOTA is, but Time Gated LSTM is one example. Another is Latent ODEs for Irregularly-Sampled Time Series.
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.
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
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
pytorch-forecasting - Time series forecasting with PyTorch
mlforecast - Scalable machine 🤖 learning for time series forecasting.
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).