Abstract
In financial field, outliers represent volatility of stock market, which plays an important role in management, portfolio selection and derivative pricing. Therefore, forecasting outliers of stock market is of the great importance in theory and application. In this paper, the problem of predicting outliers based on adaptive ensemble models of Extreme Learning Machines (ELMs) is considered. We found out that the proposed model is applicable for outlier forecasting and outperforms the methods based on autoregression (AR) and extreme learning machine (ELM) models.
Highlights
Outliers can have deleterious effects on statistical analyses
It was shown that forecast intervals are quite sensitive to additive outliers, but that point forecasts are largely unaffected unless the outlier occurs near the forecast origin
The purpose of this paper is to present adaptive ensemble model of Extreme Learning Machines (ELMs) for prediction which can lead to smaller predicting errors and more accuracy than some other forecasting methods
Summary
Outliers can have deleterious effects on statistical analyses They can result in parameter estimation biases, invalid inferences and weak volatility forecasts in financial data. Time-series data are often messed up with outliers due to the influence of unusual and non-repetitive events Forecast accuracy in such situations is decreased dramatically due to a carry-over effect of the outliers on the point forecast and a bias in the estimate of parameters. It was shown that forecast intervals are quite sensitive to additive outliers, but that point forecasts are largely unaffected unless the outlier occurs near the forecast origin. In such a situation the carry-over effect of the outlier can be quite substantial
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