On causal extrapolation of sequences with applications to forecasting

The paper suggests a method of extrapolation of notion of one-sided semi-infinite sequences representing traces of two-sided band-limited sequences; this features ensure uniqueness of this extrapolation and possibility to use this for forecasting. This lead to a forecasting method for more general s...

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Main Author: Dokuchaev, Nikolai
Format: Journal Article
Published: Elsevier Inc. 2018
Online Access:http://hdl.handle.net/20.500.11937/66221
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author Dokuchaev, Nikolai
author_facet Dokuchaev, Nikolai
author_sort Dokuchaev, Nikolai
building Curtin Institutional Repository
collection Online Access
description The paper suggests a method of extrapolation of notion of one-sided semi-infinite sequences representing traces of two-sided band-limited sequences; this features ensure uniqueness of this extrapolation and possibility to use this for forecasting. This lead to a forecasting method for more general sequences without this feature based on minimization of the mean square error between the observed path and a predicable sequence. These procedure involves calculation of this predictable path; the procedure can be interpreted as causal smoothing. The corresponding smoothed sequences allow unique extrapolations to future times that can be interpreted as optimal forecasts.
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institution Curtin University Malaysia
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publishDate 2018
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spelling curtin-20.500.11937-662212018-07-09T03:18:53Z On causal extrapolation of sequences with applications to forecasting Dokuchaev, Nikolai The paper suggests a method of extrapolation of notion of one-sided semi-infinite sequences representing traces of two-sided band-limited sequences; this features ensure uniqueness of this extrapolation and possibility to use this for forecasting. This lead to a forecasting method for more general sequences without this feature based on minimization of the mean square error between the observed path and a predicable sequence. These procedure involves calculation of this predictable path; the procedure can be interpreted as causal smoothing. The corresponding smoothed sequences allow unique extrapolations to future times that can be interpreted as optimal forecasts. 2018 Journal Article http://hdl.handle.net/20.500.11937/66221 10.1016/j.amc.2018.01.038 Elsevier Inc. restricted
spellingShingle Dokuchaev, Nikolai
On causal extrapolation of sequences with applications to forecasting
title On causal extrapolation of sequences with applications to forecasting
title_full On causal extrapolation of sequences with applications to forecasting
title_fullStr On causal extrapolation of sequences with applications to forecasting
title_full_unstemmed On causal extrapolation of sequences with applications to forecasting
title_short On causal extrapolation of sequences with applications to forecasting
title_sort on causal extrapolation of sequences with applications to forecasting
url http://hdl.handle.net/20.500.11937/66221