  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.
