mlforecast
Scalable machine 🤖 learning for time series forecasting. (by Nixtla)
neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms. (by Nixtla)
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| mlforecast | neuralforecast | |
|---|---|---|
| 11 | 84 | |
| 713 | 2,405 | |
| 5.5% | 7.7% | |
| 8.8 | 8.9 | |
| 14 days ago | 7 days 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.
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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 .
neuralforecast
Posts with mentions or reviews of neuralforecast.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-10.
- [D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)
- neuralforecast: NEW Data - star count:1877.0
- neuralforecast: NEW Data - star count:1773.0
- neuralforecast: NEW Data - star count:1749.0
- neuralforecast: NEW Data - star count:1696.0
- neuralforecast: NEW Data - star count:1663.0
What are some alternatives?
When comparing mlforecast and neuralforecast you can also consider the following projects:
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
pytorch-forecasting - Time series forecasting with PyTorch
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
nixtla - Python SDK for TimeGPT, a foundational time series model
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
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
mlforecast vs statsforecast
neuralforecast vs pytorch-forecasting
mlforecast vs tsfeatures
neuralforecast vs darts
mlforecast vs pytorch-forecasting
neuralforecast vs statsforecast
mlforecast vs darts
neuralforecast vs nixtla
mlforecast vs tsai
neuralforecast vs Prophet
mlforecast vs nixtla
neuralforecast vs flow-forecast