0000000001071180

AUTHOR

Alexandr V. Maslov

showing 3 related works from this author

DOBRO : a prediction error correcting robot under drifts

2016

We propose DOBRO, a light online learning module, which is equipped with a smart correction policy helping making decision to correct or not the given prediction depending on how likely the correction will lead to a better prediction performance. DOBRO is a standalone module requiring nothing more than a time series of prediction errors and it is flexible to be integrated into any black-box model to improve its performance under drifts. We performed evaluation in a real-world application with bus arrival time prediction problem. The obtained results show that DOBRO improved prediction performance significantly meanwhile it did not hurt the accuracy when drift does not happen.

ta113Concept driftComputer scienceMean squared prediction error02 engineering and technologyARIMAconcept drifton-line prediction error correction020204 information systems0202 electrical engineering electronic engineering information engineeringRobot020201 artificial intelligence & image processingAutoregressive integrated moving averageSimulation
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Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

2012

In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes with such data. We evaluate the performance of our method on the artificially generated and real datasets that represent different operational settings of a pilot circulating fluidized bed (CFB) reactor and CFB cold model. Our experiments suggest that QI can be used for designing very simple yet effective methods for gradual change detection in the noisy sensor data. It can be also used for detectin…

ta113Engineeringbusiness.industryOutlierBoiler (power generation)Data miningbusinesscomputer.software_genrecomputerChange detectionQuantile
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Modelling Recurrent Events for Improving Online Change Detection

2016

The task of online change point detection in sensor data streams is often complicated due to presence of noise that can be mistaken for real changes and therefore affecting performance of change detectors. Most of the existing change detection methods assume that changes are independent from each other and occur at random in time. In this paper we study how performance of detectors can be improved in case of recurrent changes. We analytically demonstrate under which conditions and for how long recurrence information is useful for improving the detection accuracy. We propose a simple computationally efficient message passing procedure for calculating a predictive probability distribution of …

ta113noiseComputer scienceData stream miningMessage passingDetectordata streamsonline change detection02 engineering and technologycomputer.software_genreTask (computing)recurrent eventschange points020204 information systems0202 electrical engineering electronic engineering information engineeringProbability distribution020201 artificial intelligence & image processingNoise (video)Data miningBaseline (configuration management)computerChange detectionProceedings of the 2016 SIAM International Conference on Data Mining
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