Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report

TL;DR

This paper introduces the Artificial Continuous Prediction Market (ACPM), a novel approach that combines multiple data sources and machine learning algorithms to predict continuous variables, demonstrating improved accuracy and robustness over traditional regression models.

Contribution

The paper presents ACPM, an innovative adaptation of prediction markets for continuous variables, incorporating learning mechanisms to handle data quality changes and participant variability.

Findings

ACPM outperforms standard regression models in predicting influenza-like illness data.

ACPM demonstrates resilience to variations in data source quality.

The approach effectively adapts to changing data and participant performance.

Abstract

We propose the Artificial Continuous Prediction Market (ACPM) as a means to predict a continuous real value, by integrating a range of data sources and aggregating the results of different machine learning (ML) algorithms. ACPM adapts the concept of the (physical) prediction market to address the prediction of real values instead of discrete events. Each ACPM participant has a data source, a ML algorithm and a local decision-making procedure that determines what to bid on what value. The contributions of ACPM are: (i) adaptation to changes in data quality by the use of learning in: (a) the market, which weights each market participant to adjust the influence of each on the market prediction and (b) the participants, which use a Q-learning based trading strategy to incorporate the market prediction into their subsequent predictions, (ii) resilience to a changing population of low- and high-performing participants. We demonstrate the effectiveness of ACPM by application to an influenza-like illnesses data set, showing ACPM out-performs a range of well-known regression models and is resilient to variation in data source quality.