  In this paper, we consider sequential online prediction (SOP) for streaming
data in the presence of outliers and change points. We propose an INstant
TEmporal structure Learning (INTEL) algorithm to address this problem. Our
INTEL algorithm is developed based on a full consideration of the duality
between online prediction and anomaly detection. We first employ a mixture of
weighted Gaussian process models (WGPs) to cover the expected possible temporal
structures of the data. Then, based on the rich modeling capacity of this WGP
mixture, we develop an efficient technique to instantly learn (capture) the
temporal structure of the data that follows a regime shift. This instant
learning is achieved only by adjusting one hyper-parameter value of the mixture
model. A weighted generalization of the product of experts (POE) model is used
for fusing predictions yielded from multiple GP models. An outlier is declared
once a real observation seriously deviates from the fused prediction. If a
certain number of outliers are consecutively declared, then a change point is
declared. Extensive experiments are performed using a diverse of real datasets.
Results show that the proposed algorithm is significantly better than benchmark
methods for SOP in the presence of outliers and change points.
