科研成果详情

To understand the behavior of complex phenomena, data collection and data analysis are the two basic key issues in this process. The most significant hard problem experimenters may face is the optimality selection of the dataset which provides valuable information about the behavior of the phenomena under the experimentation. An experiment with an optimal dataset allows more significant parameters to be estimated with minimum variance and without bias. Unfortunately, the collected datasets of many real-life experiments are not optimal due to the existence of outliers. An outlier is an observation that deviates significantly from other experimental data points arouse suspicions that it was generated by a different mechanism. The presence of even a few outliers leads to misspecification of model, biased estimation of parameters, and poor forecasts. Therefore, removing the outliers from the collected datasets is the critical and significant step before analyzing the data. This paper gives a hybrid feedforward neural network algorithm for detecting outliers as single points as well as small and large clusters in non-stationary multivariate time series using robust measures of location and dispersion matrix. From various perspectives, the performance of the proposed algorithm is compared with the existing algorithms under different scenarios using simulated datasets. © 2021 Elsevier Ltd