TGLSTM
Pytorch implementation of LSTM for irregular time series (by FedericOldani)
mlforecast
Scalable machine 🤖 learning for time series forecasting. (by Nixtla)
| TGLSTM | mlforecast | |
|---|---|---|
| 1 | 11 | |
| 11 | 732 | |
| - | 5.3% | |
| 10.0 | 8.7 | |
| about 4 years ago | 8 days ago | |
| Python | Python | |
| - | 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.
TGLSTM
Posts with mentions or reviews of TGLSTM.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-02-07.
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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.
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.
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Sales forecast for next two years
MLForecast
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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
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/mlforecast/
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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
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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.
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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.
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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)
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.
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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 .
What are some alternatives?
When comparing TGLSTM and mlforecast you can also consider the following projects:
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 🚀.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.