Inverse Signal Classification for Financial Instruments

📝 Original Info

• Title: Inverse Signal Classification for Financial Instruments
• ArXiv ID: 1303.0283
• Date: 2013-05-14
• Authors: Researchers from original ArXiv paper

📝 Abstract

The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 financial instruments traded during 2011 to identify inverse behavior among the time-series.

💡 Deep Analysis

Deep Dive into Inverse Signal Classification for Financial Instruments.

The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 financial instruments traded during 2011 to identify inverse behavior among the time-series.

📄 Full Content

Reference