Online Prediction of Exponential Decay Time Series with Human-Agent Application

Exponential decay time series are prominent in many fields. In some applications, the time series behavior can change over time due to a change in the user’s preferences or a change of environment. In this paper we present an innovative online learning algorithm, which we name Exponentron, for the prediction of exponential decay time series. We state a regret bound for our setting, which theoretically compares the performance of our online algorithm relative to the performance of the best batch prediction mechanism, which can be chosen in hindsight from a class of hypotheses after observing the entire time series. In experiments with synthetic and real-world data sets, we found that the proposed algorithm compares favorably with the classic time series prediction methods by providing up to 41% improvement in prediction accuracy. Furthermore, we used the proposed algorithm for the design of a novel automated agent for the improvement of the communication process between a driver and its automotive climate control system. Throughout extensive human study with 24 drivers we show that our agent improves the communication process and increases drivers’ satisfaction, exemplifying the Exponentron’s applicative benefit.