A ν-Support Vector Quantile Regression Model with Automatic Accuracy Control
| Autor: | Pritam Anand, Reshma Rastogi, Suresh Chandra |
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| Rok vydání: | 2022 |
| Zdroj: | Research Reports on Computer Science. :113-135 |
| ISSN: | 2811-0013 2811-0005 |
| DOI: | 10.37256/rrcs.1220221662 |
| Popis: | Quantile regression models have become popular among researchers these days. These models are being used frequently for obtaining the probabilistic forecast in different real-world applications. The Support Vector Quantile Regression (SVQR) model can obtain the conditional quantile estimate using kernel function in a non-parametric framework. The ϵ-SVQR model successfully incorporates the concept of the asymmetric ϵ-insensitive tube in the SVQR model and enables it to obtain a sparse and accurate solution. But, it requires a good choice of the user-defined parameter. A bad choice of ϵ value may result in poor predictions in the ϵ-SVQR model. In this paper, we propose a novel ‘ν-Support Vector Quantile Regression’ (ν-SVQR) model for quantile estimation. It can efficiently obtain a suitable asymmetric ϵ-insensitive zone according to the variance present in the data. The proposed ν-SVQR model uses the ν fraction of training data points for the estimation of the quantiles. In the ν-SVQR model, training points asymptotically appear above and below the asymmetric ϵ-insensitive tube in the ratio of 1 − τ and τ. Apart from these, there are other interesting properties of the proposed ν-SVQR model, which we have briefly described in this paper. These properties have been empirically verified using simulated and real-world data sets also. |
| Databáze: | OpenAIRE |
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