mlforecast
nixtlats
Our great sponsors
| mlforecast | nixtlats | |
|---|---|---|
| 11 | 2 | |
| 720 | 11 | |
| 5.5% | - | |
| 8.7 | 0.0 | |
| 14 days ago | about 2 years ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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
-
Sales forecast for next two years
MLForecast
-
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
-
Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/mlforecast/
-
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
-
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.
-
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.
-
[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.
-
[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.
-
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 .
nixtlats
-
Automated Time Series Processing and Forecasting
Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On our side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.
About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.
What are some alternatives?
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
nixtla - Python SDK for TimeGPT, a foundational time series model
tsfeatures - Calculates various features from time series data. Python implementation of the R package tsfeatures.
Time-Series-Transformer - A data preprocessing package for time series data. Design for machine learning and deep learning.
pytorch-forecasting - Time series forecasting with PyTorch
darts - A python library for user-friendly forecasting and anomaly detection on time series.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
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
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).