mlforecast
jidt
| mlforecast | jidt | |
|---|---|---|
| 11 | 1 | |
| 732 | 247 | |
| 5.3% | - | |
| 8.7 | 6.0 | |
| 8 days ago | 27 days ago | |
| Python | Java | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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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 .
jidt
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Time series modeling using ARIMA and XGBoost. Intro to free time series modeling resources
IDK why this got downvoted, it's good to know there's a non-linear alternative to ARIMA. Here's J Lizier's implementation; it's in Java, but apparently usable in Python via Jpype.
What are some alternatives?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
ennemi - Easy Nearest Neighbor Estimation of Mutual Information
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
Selenium WebDriver - A browser automation framework and ecosystem.
pytorch-forecasting - Time series forecasting with PyTorch
Graal - GraalVM compiles Java applications into native executables that start instantly, scale fast, and use fewer compute resources 🚀
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
Inform - A cross platform C library for information analysis of dynamical systems
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).