ML methods and models to run in real-time and online scenarios
Mikhail (Michael) Maltsev
Data & Analytics Executive | Digging gems out of the data
Continuing the discussion on handling sub-millisecond transactions on the fly, let’s explore one of the fastest and most efficient machine learning models for real-time anomaly detection—Hoeffding Trees (also known as Very Fast Decision Trees - VFDT).
Why Traditional ML Fails in Ultra-Fast Streams
Imagine dealing with an infinite, high-speed data stream, where new information arrives faster than you can analyze it. Traditional decision trees, Random Forests, and even some neural networks won’t work because:
? They require all the data upfront before making decisions.
? They store too much data in memory, making them slow and inefficient.
? They don’t adapt dynamically to changing data patterns.
This is where Hoeffding Trees shine. Unlike standard decision trees, they learn incrementally—processing millions of trades per second without needing all the data in memory.
Key Technical Advantages of the Hoeffding Trees for Streaming Data:
Time Complexity: O(1) per sample (constant-time updates)
Memory Usage: O(log N) (only necessary statistics stored)
Training Speed: Millions of samples per second on commodity hardware
Inference Speed: Sub-microsecond per sample
The Secret Sauce: Hoeffding Bound
The power of Hoeffding Trees comes from the Hoeffding Bound, a mathematical inequality that ensures optimal decision-making with minimal data.
? It quantifies the probability that an estimated mean deviates from the true mean.
? Instead of waiting for all data, the model only splits nodes when there is high-confidence evidence that one feature is better than another.
? This enables real-time learning, ensuring trees grow only when necessary, keeping them fast and efficient.
How Fast Do Hoeffding Trees Learn?
? Unlike traditional decision trees, Hoeffding Trees do NOT split nodes immediately.
? They accumulate statistics and wait until enough confidence is reached before making a split.
? The Hoeffding Bound ensures that splitting happens only when necessary, preventing overfitting and keeping training ultra-fast.
This makes them ideal for HFT, where trade execution anomalies must be detected in microseconds.
Alternatives to Hoeffding Trees for Real-Time Learning
1. Online Random Forests
?? Like traditional Random Forests, but built for streaming data.
?? Uses multiple Hoeffding Trees to improve accuracy.
?? Downside: Requires more memory due to multiple trees being stored.
2. Adaptive Boosting (AdaBoost) for Streaming Data
?? Adjusts to new data dynamically by reweighting misclassified instances.
?? Downside: Takes longer to converge than Hoeffding Trees.
Handling Concept Drift: Keeping Up with a Changing Market
One of the biggest challenges in HFT anomaly detection is concept drift—when market behaviour changes over time.
For example:
领英推荐
A fraud detection model trained on past trade behaviour may fail if fraudsters change tactics.
Execution speed expectations may shift as infrastructure improves.
Adaptive Hoeffding Trees for Concept Drift
?? Continuously monitors new trade execution data.
?? Detects shifts in trade execution behaviour.
?? Automatically updates splits or replaces branches when needed.
Think of it like adjusting sails to changing winds - keeping the decision tree flexible as the market evolves.
Real-World Performance of Hoeffding Trees in HFT
Data Throughput in Practice
?? On a GPU (CUDA-based implementation): 50M+ trades per second
?? On an FPGA (custom hardware): 100M+ trades per second (microsecond inference)
Hoeffding Trees can run on a single CPU core, but they truly shine when deployed on GPUs, FPGAs, or specialized real-time processing hardware.
Optimizing Hoeffding Trees for HFT Monitoring
1. Parallel Processing for Speed
2. Reducing False Positives
3. Hardware Optimizations for Ultra-Low Latency
In case you want to dig deeper, here are some articles worth looking into:
1. “Mining High-Speed Data Streams” by Pedro Domingos and Geoff Hulten (2000)
2. “A Beginner’s Guide to Hoeffding Tree with Python Implementation” by We Talk Data (August 2020)
3. “Online Machine Learning for Anomaly Detection in Time Series Data” by Sebastian Wette and Florian Heinrichs (September 2024)
4. “Fuzzy Hoeffding Decision Tree for Data Stream Classification” by Atlantis Press (2023)
5. “Machine Learning Approaches to Time Series Anomaly Detection” by Anomalo (2022)
6. “Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams” by arXiv (November 2024)
7. “Real-Time Anomaly Detection: Use Cases and Code Examples” by Tinybird (2022)
8. “Anomaly Detection in Streaming Time Series Data with Online Learning Using Amazon Managed Service for Apache Flink” by AWS Machine Learning Blog (2023)
9. “Introducing Hoeffding’s Inequality for Creating Storage-Less Decision Trees” by Towards Data Science (April 2021)
10. “Anomaly Detection Using AI & Machine Learning” by Nile (2023)
Delivery Manager
1 周Looks interesting. Does it basically uses statistical validation before splitting?
Leading Tech Innovations
1 周I learned something new today, thank you!