The paper discusses the superiority of Kalman Filter (KF) over neural networks in some cases and the need to optimize KF parameters. Despite its 60-year-old linear architecture, the KF outperformed a fancy neural network after parameter optimization. The study emphasizes the importance of optimizing KF and not relying on its assumptions, offering a simple training procedure available on PyPI.
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Why Neural Networks May Seem Better than the Kalman Filter (KF) and How to Improve Your KF
Background
The Kalman Filter (KF) has been a widely used method for sequential forecasting and control since 1960. Despite the introduction of new methods, the KF’s simple design makes it practical, robust, and competitive. Our recent paper from NeurIPS 2023 introduces our work on this topic, with code available on PyPI.
Kalman Filter or a Neural Network?
We experimented with a neural network on top of the KF and found that by optimizing the KF parameters, we achieved better prediction accuracy than with the neural network alone. This highlights the importance of optimizing the KF in comparison to other methods.
Optimizing the Kalman Filter
Our research revealed that the standard closed-form equations for KF parameters do not always yield optimal predictions in real-world scenarios. It’s important to optimize the KF parameters to minimize prediction errors, similar to other prediction models.
How to Optimize the KF?
By treating the KF parameters as model parameters and using techniques such as Cholesky decomposition, we can optimize the KF efficiently and effectively. This optimization procedure has shown to be fast and stable in our experiments.
Summary
Our main message is that the KF assumptions cannot always be trusted, and therefore it’s crucial to optimize the KF directly. Our simple training procedure is available in PyPI, allowing for easy upgrade of existing KF systems to the optimized version.
AI Solutions for Middle Managers
If you want to evolve your company with AI and stay competitive, consider using Optimization or Architecture: How to Hack Kalman Filtering. Discover how AI can redefine your way of work and identify automation opportunities, define KPIs, select AI solutions, and implement gradually for your advantage.
Spotlight on a Practical AI Solution
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This HTML code provides a simple and clear presentation of the text with highlights of practical solutions and value. It includes the main points about optimizing the Kalman Filter, as well as a spotlight on a practical AI solution for middle managers.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Optimization or Architecture: How to Hack Kalman Filtering
- Towards Data Science – Medium
- Twitter – @itinaicom
