کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
997596 1481456 2011 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Holt’s exponential smoothing and neural network models for forecasting interval-valued time series
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری کسب و کار و مدیریت بین المللی
پیش نمایش صفحه اول مقاله
Holt’s exponential smoothing and neural network models for forecasting interval-valued time series
چکیده انگلیسی

Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively. In Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval-valued stock market time series.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 27, Issue 3, July–September 2011, Pages 740–759
نویسندگان
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