Forecasting e-waste recovery scale driven by seasonal data characteristics : A decomposition-ensemble approach
Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of 'decomposition-integration', considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova-Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt-Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt-Winters model, seasonal autoregressive integrated moving average model and SVR model).
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2022 |
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Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:40 |
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Enthalten in: |
Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA - 40(2022), 7 vom: 26. Juli, Seite 870-881 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Mohsin, Akm [VerfasserIn] |
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Links: |
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Themen: |
Data trait-driven modelling |
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Anmerkungen: |
Date Completed 17.05.2022 Date Revised 17.05.2022 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1177/0734242X211061443 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM333605470 |
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520 | |a Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of 'decomposition-integration', considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova-Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt-Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt-Winters model, seasonal autoregressive integrated moving average model and SVR model) | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a E-waste | |
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700 | 1 | |a Masum Iqbal, Mohammed |e verfasserin |4 aut | |
700 | 1 | |a Salim, Zahir Rayhan |e verfasserin |4 aut | |
700 | 1 | |a Hossain, Alamgir |e verfasserin |4 aut | |
700 | 1 | |a Al Kafy, Abdullah |e verfasserin |4 aut | |
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