Journal of Changshu Institute of Technology2024，Vol.38Issue(2)：89-94,6.
基于LLM的金融市场波动率高频数据异常检测方法
Method of Detection of High-frequency Data Anomaly in Financial Market Volatility Based on LLM
摘要
Abstract
High-frequency data in financial markets usually has high-dimensional characteristics, including time-series data and other macroeconomic indicators. The processing of high-dimensional data requires more sophisticated algorithms, leading to increased computational complexity and the risk of model over-fitting. In view of this, the anomaly detection method of financial market volatility high-frequency data based on LLM is proposed. Specifically, the method sets to standardization of the daily average sequence of each high-frequency data. While screening data, the method uses standardized processing to set the relevant threshold, transforms the data of different dimensions into the same scale, and applies the connected algorithm to put edge-connected financial market volatility high-frequency data into a same group. When calculating high-frequency data threshold to be detected, the method uses local linear mapping so as to bring the financial market volatility high-frequency data anomaly detection to an end. The experimental results show that when the proposed method is 0.98, the ROC curve displays a stable run and, with the contribution factor being 1.287, and the reconstruction error being 1.6％, the loss value of anomaly detection in the training set reaches a steady state at the fastest speed.关键词
局部线性映射/金融市场/波动率/高频数据/异常检测Key words
local linear mapping/financial markets/volatility/high-frequency data/anomaly detection分类
信息技术与安全科学引用本文复制引用
何远景,李光龙..基于LLM的金融市场波动率高频数据异常检测方法[J].Journal of Changshu Institute of Technology,2024,38(2):89-94,6.基金项目
2021年安徽省省级质量工程项目"《保险理论与实务》精品课程"(2021jpk028) （2021jpk028）
教育部职业教育提质培优行动计划(2020—2023年)"《大数据视域下,高校思政教育协同育人建构路径研究——基于金融类专业分析》"(皖教秘高[2021]35号) （2020—2023年）
安徽省职业与成人教育学会2022年度教育教学研究规划课题"大思政"背景下,高职金融学课程群协同育人建构路径研究"(Azj2022091) （Azj2022091）
2020年院级质量工程项目"《保险理论与实务》线下课程"(2020yxxkc01) （2020yxxkc01）
