NeuralProphet: An Introduction to Human-Centered Forecasting
NeuralProphet stands out as a powerful and intuitive framework for time series forecasting, guiding users toward creating interpretable models with ease. Developed with the flexibility of human-centered design, this tool is ideal for iterative model building, where users can swiftly create, analyze, and refine their forecasting models. Here’s a closer look at what makes NeuralProphet a notable choice for data scientists and analysts alike.
What is NeuralProphet?
NeuralProphet is a versatile forecasting tool grounded in PyTorch, which integrates the strengths of neural networks with traditional time-series analysis methods. Drawing inspiration from frameworks like Facebook Prophet and AR-Net, it empowers users to define, customize, visualize, and evaluate forecasting models with minimal code. Its development is ongoing, with contributions and feedback being actively encouraged to enhance its features and usability.
Key Features
NeuralProphet is tailored for higher-frequency, longer-duration time series data, making it suitable for datasets with sub-daily granularity spanning years. Here’s what you can expect from this tool:
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Autoregression and Trends: NeuralProphet includes autocorrelation modeling and piecewise linear trends with automatic changepoint detection, offering both linear and neural network (AR-Net) options.
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Seasonality and Regressors: The framework supports modeling of seasonal effects through Fourier terms and handles both lagged and future regressors, allowing for dynamic data features like temperature adjustments.
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Event and Global Modeling: Users can factor in events, such as country holidays, and deploy global, local, or hybrid ('glocal') modeling strategies.
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Advanced Analysis: NeuralProphet provides facilities for uncertainty estimation via quantile regression, cross-validation, and rigorous component regularization.
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Visualization: Users can plot forecast components and model coefficients, gaining enhanced insight into model behavior.
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Scalable Features: It includes provisions for working with multiple time series, model checkpointing, and validation, ensuring robust and reliable forecasting capabilities.
Get Started
To start using NeuralProphet, installation is straightforward via pip:
pip install neuralprophet
For those interested in using the package in Jupyter notebooks or seeking the latest version, special installation options are available including a 'live' version for dynamic plotting of training loss.
Getting Involved
NeuralProphet is an open-source project, actively seeking contributions and community engagement. Potential contributors can find guidance and resources for participation, including discussion forums on GitHub and a vibrant Slack community for real-time interaction.
Tutorial and Documentation
Comprehensive tutorials and documentation are accessible, offering step-by-step guides through example notebooks. These resources ensure users can seamlessly onboard into the NeuralProphet ecosystem, taking advantage of the platform’s full potential.
Example Code
Here's a minimal example to illustrate how straightforward it is to use NeuralProphet:
from neuralprophet import NeuralProphet
m = NeuralProphet()
metrics = m.fit(df)
forecast = m.predict(df)
Visualization is an essential feature and can be executed with inbuilt plotting functions:
fig_forecast = m.plot(forecast)
fig_components = m.plot_components(forecast)
Future Developments
NeuralProphet continues to evolve, with future enhancements in the pipeline such as cross-relation of lagged regressors and logistic growth models. Users are encouraged to keep an eye on the releases page for the latest updates.
Community and Collaboration
NeuralProphet thrives on community involvement. From feedback and suggestions to new feature development, contributors are at the heart of this project’s ongoing journey. Interested individuals can contact the project lead or join community discussions to begin their journey with NeuralProphet.
Embrace the power of human-centered design and forecasting precision with NeuralProphet, your reliable companion in time series analysis.