Online Prediction via Continuous Artificial Prediction Markets

Prediction markets are well-established tools for aggregating information from diverse sources into accurate forecasts. Their success has been demonstrated in a wide range applications, including presidential campaigns, sporting events and economic outcomes. Recently, they have been introduced to the machine-learning community in the form of Artificial Prediction Markets, whereby algorithms trade contracts reflecting their levels of confidence. To date, those markets have mostly been studied in the context of offline classification, with quite promising results. We extend those markets to enable their use in online regression, and introduce: (i) adaptive trading strategies informed by individual trading history; and (ii) the ability of participants to revise their predictions by reflecting upon the wisdom of the crowd, which is manifested in the collective performance of the market. We empirically evaluate our model using multiple UCI data sets, and show that it outperforms several well-established techniques from the literature on online regression.