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Data Improving in Time Series Using ARX and ANN Models
2017
IEEE Transactions on Power Systems
Anomalous data can negatively impact energy forecasting by causing model parameters to be incorrectly estimated. This paper presents two approaches for the detection and imputation of anomalies in time series data. Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models are used to extract the characteristics of time series. Anomalies are detected by performing hypothesis testing on the extrema of the residuals, and the anomalous data points are imputed using the
doi:10.1109/tpwrs.2017.2656939
fatcat:gk7ivs4jjjcpxlk67rqw2cdp6i