---
title: "TensorLOB: Structure-Preserving Tensor Learning for Limit Order Book Forecasting"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.02866v1"
permalink: "https://feed.astralor.com/items/itm_1d242a1ecf7f60173536c71f"
published: "2026-11-02T10:03:43.000Z"
score: 56
channel: "tech"
tags: ["推理效率","TensorLOB","Structure-Preserving Tensor Learning","Limit Order Book","Forecasting The","LOB","Its"]
---

# TensorLOB: Structure-Preserving Tensor Learning for Limit Order Book Forecasting

## 速览

The limit order book (LOB) records outstanding buy and sell orders across multiple price levels. Its state evolves over time across market side, price level, and order attribute, naturally forming a multiway structure. …

## 关键点

- The limit order book (LOB) records outstanding buy and sell orders across multiple price levels.
- Its state evolves over time across market side, price level, and order attribute, naturally forming a multiway structure.
- However, existing LOB models typically flatten these dimensions into a matrix model, thus obscuring the existing structural relationships.

## 全文

The limit order book (LOB) records outstanding buy and sell orders across multiple price levels. Its state evolves over time across market side, price level, and order attribute, naturally forming a multiway structure. However, existing LOB models typically flatten these dimensions into a matrix model, thus obscuring the existing structural relationships. Starting from first principles and the market-microstructure intuition of the LOB, we design TensorLOB to operate entirely in the tensor domain. TensorLOB uses mode-wise tensor contractions for feature extraction and directly contracts the learned representation with low-rank class-weight tensors for prediction, retaining the distinct roles of time, side, level, and attribute modes. Experiments on the FI-2010 benchmark show that TensorLOB achieves the highest accuracy, recall, and F1 score across all four evaluated horizons. This proof of concept highlights the potential of high-dimensional modelling of multi-way latent information intrinsic to financial markets, which has so far been largely overlooked.

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