LazyProphet
statsforecast
| LazyProphet | statsforecast | |
|---|---|---|
| 2 | 58 | |
| 74 | 3,591 | |
| - | 3.4% | |
| 0.0 | 8.9 | |
| over 1 year ago | 4 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.
statsforecast
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TimeGPT-1
I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
- Facebook Prophet: library for generating forecasts from any time series data
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Sales forecast for next two years
If you only have historical data: StatsForecast
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Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
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Demand Planning
If you are mostly worried about time and use python you could try out Nixtla's statsforecast as it is very snappy. https://github.com/Nixtla/statsforecast
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Statistical vs Machine Learning vs Deep Learning Modeling for Time Series Forecasting
I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/statsforecast/
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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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[Discussion] Amazon's AutoML vs. open source statistical methods
In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods.
- Statistical methods outperform Amazon’s ML Forecast
What are some alternatives?
ThymeBoost - Forecasting with Gradient Boosted Time Series Decomposition
darts - A python library for user-friendly forecasting and anomaly detection on time series.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
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 🚀.
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
fable - Tidy time series forecasting
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
pytorch-forecasting - Time series forecasting with PyTorch
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
hts - Hierarchical and Grouped Time Series