Concept
Self-organizing Maps
Self-organizing Maps are Machine Learning concepts. The Library holds 1 implementations, each one a working definition you can pull into Quant.
Top Self-organizing Maps indicators
1 total
What are Self-organizing Maps?
A self-organizing map (SOM), introduced by Teuvo Kohonen, is an unsupervised neural network that projects high-dimensional data onto a low-dimensional grid of nodes, usually two-dimensional, while preserving topology: similar inputs land on nearby nodes. Training presents feature vectors one at a time; the best-matching node and its grid neighbors are pulled toward each input, with the neighborhood radius and learning rate shrinking as training proceeds. Each node ends up holding a prototype, a representative combination of feature values.
For markets, the grid becomes a map of conditions: feed in volatility, momentum, and volume features and regions of the map come to correspond to regimes, with the live bar's best-matching node saying where conditions currently sit. The approach is closely related to K-means regime clustering, since both learn prototypes. Its supervised sibling, learning vector quantization (LVQ), attaches class labels to the prototypes so the nearest one's label becomes a directional signal, which is how some charting implementations of this family work.
How traders use it
- Regime mapping: train the map on historical feature vectors, label coherent regions (quiet trend, volatile range), and key strategy logic off which region the current bar maps into.
- Analog finding: the best-matching node identifies which historical conditions most resemble the present, a prototype-based cousin of kNN analog forecasting.
- LVQ classification: prototypes carry bullish or bearish outcome labels and are nudged toward inputs of their own class during training; live classification is simply the nearest prototype's label.
Related concepts · Learned models
Concept family
Machine Learning
32 concepts mapped · 21 in the Library
Self-organizing Maps FAQ
How does a self-organizing map differ from K-means?
Both learn a set of prototypes, but a SOM arranges them on a grid where neighboring prototypes are constrained to stay similar, while K-means clusters have no spatial relationship to each other. That topology lets a SOM show how regimes relate, whether the market drifted to an adjacent state or jumped across the map, which a plain cluster label cannot express.
What is LVQ and how does it relate to SOMs?
Learning vector quantization is the supervised member of the same Kohonen family. Prototypes are assigned class labels and, during training, move toward inputs of their own class and away from others; prediction returns the nearest prototype's label. Trading implementations often prefer LVQ because it yields a directional output directly, whereas a raw SOM only says which region of the map is active.
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