LazyProphet
mlforecast
| LazyProphet | mlforecast | |
|---|---|---|
| 2 | 11 | |
| 74 | 732 | |
| - | 5.3% | |
| 0.0 | 8.7 | |
| over 1 year ago | 7 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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LazyProphet
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XGBoost for time series
If you want a quick thing to try for single time series you can try my package: LazyProphet which uses LightGBM under the hood.
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Best Python library for time series univariant stationary data prediction?[D]
If you are feeling adventurous you could try some of my packages: ThymeBoost or LazyProphet. ThymeBoost is interesting as it is gradient boosting around time series decomposition. So you will still have the trend/seasonality decomposition but with more exotic methods. LazyProphet is just some feature engineering for time series fed into Lightgbm but it tends to perform well enough. Both tend to outperform fbprophet although that generally isn't too hard to do and they both have automatic fitting procedure that performs ok.
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?
ThymeBoost - Forecasting with Gradient Boosted Time Series Decomposition
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
pytorch-forecasting - Time series forecasting with PyTorch
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
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
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 🚀.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
TGLSTM - Pytorch implementation of LSTM for irregular time series