English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we consider sequential online prediction (SOP) for streamingdata in the presence of outliers and change points. We propose an INstantTEmporal structure Learning (INTEL) algorithm to address this problem. OurINTEL algorithm is developed based on a full consideration of the dualitybetween online prediction and anomaly detection. We first employ a mixture ofweighted Gaussian process models (WGPs) to cover the expected possible temporalstructures of the data. Then, based on the rich modeling capacity of this WGPmixture, we develop an efficient technique to instantly learn (capture) thetemporal structure of the data that follows a regime shift. This instantlearning is achieved only by adjusting one hyper-parameter value of the mixturemodel. A weighted generalization of the product of experts (POE) model is usedfor fusing predictions yielded from multiple GP models. An outlier is declaredonce a real observation seriously deviates from the fused prediction. If acertain number of outliers are consecutively declared, then a change point isdeclared. Extensive experiments are performed using a diverse of real datasets.Results show that the proposed algorithm is significantly better than benchmarkmethods for SOP in the presence of outliers and change points.

Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach