Handling Missing Data from Heteroskedastic and Nonstationary Data

Abstract

This paper presents a computational intelligence approach for predicting missing data in the presence of concept drift using an ensemble of multi-layered feed forward neural networks. An algorithm that detects concept drift by measuring heteroskedasticity is proposed. Six instances prior to the occurrence of missing data are used to approximate the missing values. The algorithm is applied to simulated time series data sets resembling non-stationary data from a sensor. Results show that the prediction of missing data in non-stationary time series data is possible but is still a challenge. For one test, up to 78% of the data could be predicted within 10% tolerance range of accuracy.KeywordsStock MarketChaotic SystemTime Series DataConcept DriftConditional HeteroskedasticityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.