darts VS mlforecast

Compare darts vs mlforecast and see what are their differences.

darts

A python library for user-friendly forecasting and anomaly detection on time series. (by unit8co)

mlforecast

Scalable machine 🤖 learning for time series forecasting. (by Nixtla)
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darts mlforecast
47 11
7,159 693
3.1% 7.6%
9.1 8.8
about 18 hours ago 22 days ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

darts

Posts with mentions or reviews of darts. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-10.

mlforecast

Posts with mentions or reviews of mlforecast. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-25.
  • Sales forecast for next two years
    2 projects | /r/datascience | 25 Jun 2023
    MLForecast
  • Demand Planning
    2 projects | /r/datascience | 27 Mar 2023
    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?
    2 projects | /r/datascience | 15 Mar 2023
    - https://nixtla.github.io/mlforecast/
  • XGBoost for time series
    3 projects | /r/datascience | 9 Mar 2023
    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
    3 projects | /r/datascience | 15 Feb 2023
    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
    2 projects | /r/datascience | 1 Aug 2022
    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].
    10 projects | /r/MachineLearning | 22 Feb 2022
    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)
    13 projects | /r/MachineLearning | 7 Feb 2022
    GluonTS Differences: -GluonTS is written in mxnet, which reduces its adoption. In contrast, NeuralForecast is written in PyTorch. -Including new models in GluonTS tends to be challenging because mxnet 's and the library structure's learning curve are steep. PyTorch-Forecasting Differences: -NeuralForecast hosts some models from our research, including N-HiTS and Transformer-based (Autoformer, Informer, Transformer, etc.) methods specialized in long-horizon forecasting (https://arxiv.org/abs/2201.12886). -And the exogenous variables extension of N-BEATS, the NBEATSx (https://arxiv.org/abs/2104.05522). Extra Features: -NeuralForecast has a wide range of curated datasets used in research to develop and test new models, such as Tourism, M3, M4, M5, EPF, ILI, Traffic, Weather, etc. -NeuralForecast models include reasonable hyperparameter spaces to speed up hyperparameter search, based on our experience. -We include an experiment module that makes it easy to put the entire time series forecasting pipeline into production. -Finally, NeuralForecast is part of a larger ecosystem of time-series analysis and forecasting that includes feature creation (tsfeatures, https://github.com/Nixtla/tsfeatures), machine learning models (mlforecast, https://github.com/Nixtla/mlforecast) and statistical models (statsforecast, https://github.com/Nixtla/statsforecast).
    13 projects | /r/MachineLearning | 7 Feb 2022
    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.
    13 projects | /r/MachineLearning | 7 Feb 2022
    We know that there is no generic solution and that each dataset requires particular work; however, in addition to NeuralForecast we have other time series processing libraries for faster iteration such as MLForecast (https://github.com/Nixtla/mlforecast) and StatsForecast (https://github.com/Nixtla/statsforecast). In addition, NeuralForecast includes models that have demonstrated excellent performance in different datasets and scenarios, such as NHITS (https://arxiv.org/abs/2201.12886).

What are some alternatives?

When comparing darts and mlforecast you can also consider the following projects:

sktime - A unified framework for machine learning with time series

pytorch-forecasting - Time series forecasting with PyTorch

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

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

statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.

nixtla - Python SDK for TimeGPT, a foundational time series model

neural_prophet - NeuralProphet: A simple forecasting package

neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.

greykite - A flexible, intuitive and fast forecasting library

flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).