# Computational intensive methods for prediction and imputation in time series analysis

• Volume: 31, Issue: 1-2, page 121-139
• ISSN: 1509-9423

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## Abstract

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One of the main goals in times series analysis is to forecast future values. Many forecasting methods have been developed and the most successful are based on the concept of exponential smoothing, based on the principle of obtaining forecasts as weighted combinations of past observations. Classical procedures to obtain forecast intervals assume a known distribution for the error process, what is not true in many situations. A bootstrap methodology can be used to compute distribution free forecast intervals. First an adequately chosen model is fitted to the data series. Afterwards, and inspired on sieve bootstrap, an AR(p) is used to filter the series of the random component, under the stationarity hypothesis. The centered residuals are then resampled and the initial series is reconstructed. This methodology will be used to obtain forecasting intervals and for treating missing data, which often appear in a real time series. An automatic procedure was developed in R language and will be applied in simulation studies as well as in real examples.

## How to cite

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Maria Manuela Neves, and Clara Cordeiro. "Computational intensive methods for prediction and imputation in time series analysis." Discussiones Mathematicae Probability and Statistics 31.1-2 (2011): 121-139. <http://eudml.org/doc/277036>.

@article{MariaManuelaNeves2011,
abstract = {One of the main goals in times series analysis is to forecast future values. Many forecasting methods have been developed and the most successful are based on the concept of exponential smoothing, based on the principle of obtaining forecasts as weighted combinations of past observations. Classical procedures to obtain forecast intervals assume a known distribution for the error process, what is not true in many situations. A bootstrap methodology can be used to compute distribution free forecast intervals. First an adequately chosen model is fitted to the data series. Afterwards, and inspired on sieve bootstrap, an AR(p) is used to filter the series of the random component, under the stationarity hypothesis. The centered residuals are then resampled and the initial series is reconstructed. This methodology will be used to obtain forecasting intervals and for treating missing data, which often appear in a real time series. An automatic procedure was developed in R language and will be applied in simulation studies as well as in real examples.},
author = {Maria Manuela Neves, Clara Cordeiro},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {bootstrap; forecast intervals; missing data; time series analysis},
language = {eng},
number = {1-2},
pages = {121-139},
title = {Computational intensive methods for prediction and imputation in time series analysis},
url = {http://eudml.org/doc/277036},
volume = {31},
year = {2011},
}

TY - JOUR
AU - Maria Manuela Neves
AU - Clara Cordeiro
TI - Computational intensive methods for prediction and imputation in time series analysis
JO - Discussiones Mathematicae Probability and Statistics
PY - 2011
VL - 31
IS - 1-2
SP - 121
EP - 139
AB - One of the main goals in times series analysis is to forecast future values. Many forecasting methods have been developed and the most successful are based on the concept of exponential smoothing, based on the principle of obtaining forecasts as weighted combinations of past observations. Classical procedures to obtain forecast intervals assume a known distribution for the error process, what is not true in many situations. A bootstrap methodology can be used to compute distribution free forecast intervals. First an adequately chosen model is fitted to the data series. Afterwards, and inspired on sieve bootstrap, an AR(p) is used to filter the series of the random component, under the stationarity hypothesis. The centered residuals are then resampled and the initial series is reconstructed. This methodology will be used to obtain forecasting intervals and for treating missing data, which often appear in a real time series. An automatic procedure was developed in R language and will be applied in simulation studies as well as in real examples.
LA - eng
KW - bootstrap; forecast intervals; missing data; time series analysis
UR - http://eudml.org/doc/277036
ER -

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