The combination of forecasts with different time aggregation

In forecasting, it is important to improve forecast accuracy. Thus, the forecast combination have been proposed in the literature. Usually, the classical approach in forecast combination obtain from the composite of two (or more) available forecasts with identical timings. However, fore...

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Main Authors: Abd Rahman, Nur Haizum, Lee, Muhammad Hisyam
Format: Article
Language:English
Published: Academy of Sciences Malaysia 2019
Online Access:http://psasir.upm.edu.my/id/eprint/82372/1/The%20combination%20of%20forecasts%20with%20different%20time%20aggregation%20.pdf
http://psasir.upm.edu.my/id/eprint/82372/
https://www.akademisains.gov.my/asmsj/asm-sc-j-vol-12-special-issue-5-2019-for-icoaims2019/
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spelling my.upm.eprints.823722021-08-12T02:20:23Z http://psasir.upm.edu.my/id/eprint/82372/ The combination of forecasts with different time aggregation Abd Rahman, Nur Haizum Lee, Muhammad Hisyam In forecasting, it is important to improve forecast accuracy. Thus, the forecast combination have been proposed in the literature. Usually, the classical approach in forecast combination obtain from the composite of two (or more) available forecasts with identical timings. However, forecast horizon, short and long term do affect the forecast performance. Therefore, unlike previous combinations, this paper combined the forecasts with different time aggregations in order to capture the unique information of the data set. We had considered the problems in forecasting daily air pollutant index (API) as well as the monthly aggregate, by using the Box-Jenkins method and fuzzy time series methodas the time series approach. Then, the monthly aggregate forecasts were interpolated to obtain the forecasts on a daily basis. Each of the original forecasts was used to determine the weights in forming the combined forecast. The error magnitude measurements were used to measure the accuracy. The result showed that the forecast combinations with different timings outperformed the individual forecasts and traditional forecast combinations with identical timing. Hence, the combination of different timing data sets produced better forecasting accuracy, which can be a good practice in many types of data with different time horizon. Academy of Sciences Malaysia 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82372/1/The%20combination%20of%20forecasts%20with%20different%20time%20aggregation%20.pdf Abd Rahman, Nur Haizum and Lee, Muhammad Hisyam (2019) The combination of forecasts with different time aggregation. ASM Science Journal, 12 (spec. 5). pp. 160-166. ISSN 1823-6782 https://www.akademisains.gov.my/asmsj/asm-sc-j-vol-12-special-issue-5-2019-for-icoaims2019/
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In forecasting, it is important to improve forecast accuracy. Thus, the forecast combination have been proposed in the literature. Usually, the classical approach in forecast combination obtain from the composite of two (or more) available forecasts with identical timings. However, forecast horizon, short and long term do affect the forecast performance. Therefore, unlike previous combinations, this paper combined the forecasts with different time aggregations in order to capture the unique information of the data set. We had considered the problems in forecasting daily air pollutant index (API) as well as the monthly aggregate, by using the Box-Jenkins method and fuzzy time series methodas the time series approach. Then, the monthly aggregate forecasts were interpolated to obtain the forecasts on a daily basis. Each of the original forecasts was used to determine the weights in forming the combined forecast. The error magnitude measurements were used to measure the accuracy. The result showed that the forecast combinations with different timings outperformed the individual forecasts and traditional forecast combinations with identical timing. Hence, the combination of different timing data sets produced better forecasting accuracy, which can be a good practice in many types of data with different time horizon.
format Article
author Abd Rahman, Nur Haizum
Lee, Muhammad Hisyam
spellingShingle Abd Rahman, Nur Haizum
Lee, Muhammad Hisyam
The combination of forecasts with different time aggregation
author_facet Abd Rahman, Nur Haizum
Lee, Muhammad Hisyam
author_sort Abd Rahman, Nur Haizum
title The combination of forecasts with different time aggregation
title_short The combination of forecasts with different time aggregation
title_full The combination of forecasts with different time aggregation
title_fullStr The combination of forecasts with different time aggregation
title_full_unstemmed The combination of forecasts with different time aggregation
title_sort combination of forecasts with different time aggregation
publisher Academy of Sciences Malaysia
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/82372/1/The%20combination%20of%20forecasts%20with%20different%20time%20aggregation%20.pdf
http://psasir.upm.edu.my/id/eprint/82372/
https://www.akademisains.gov.my/asmsj/asm-sc-j-vol-12-special-issue-5-2019-for-icoaims2019/
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score 13.209306