eli5
mlforecast
| eli5 | mlforecast | |
|---|---|---|
| 1 | 11 | |
| 2,730 | 720 | |
| 0.8% | 3.8% | |
| 0.0 | 8.7 | |
| about 2 years ago | 18 days ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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.
eli5
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How to extract keywords important to a text classification problem?
https://github.com/TeamHG-Memex/eli5 can help you.
mlforecast
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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?
lime - Lime: Explaining the predictions of any machine learning classifier
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
pytorch-forecasting - Time series forecasting with PyTorch
course-nlp - A Code-First Introduction to NLP course
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
graph_summarizer - summarize text using graphs and language vector models
darts - A python library for user-friendly forecasting and anomaly detection on time series.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
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