NDLI: Interval function and its linear least-squares approximation

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Interval function and its linear least-squares approximation

Content Provider	ACM Digital Library
Author	Hu, Chenyi
Abstract	This paper reports an interval least-squares (ILS) algorithm that computationally approximates an interval function, in which both dependent and independent variables are interval valued. An initial version of this algorithm and its implementation were developed as a computational scheme for the stock market variability forecast with significantly improved results reported in [16] and [18]. The computational scheme then applied to predict mortgage rate [12], crude oil price [31], and network bandwidth [23]. In all of these applications, the interval least squares approach improved computational results significantly in terms of much higher accuracy and stability comparing against that of traditional confidence interval forecasts with point-valued ordinary least squares. In this paper, we modify the scheme as a general purpose ILS algorithm for interval function approximation with clearly defined concepts and quantitative quality assessment. A summary of its application on a real world multi-variable time series is also included.
Starting Page	16
Ending Page	23
Page Count	8
File Format	PDF
ISBN	9781450305150
DOI	10.1145/2331684.2331689
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interval least squares Uncertainty Interval function Variability forecast Error and accuracy of interval estimation
Content Type	Text
Resource Type	Article

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