A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction

Wang, Piao; Chudhery, Muhammad Adnan Zahid; Xu, Jilan; Zhao, Xin; Wang, Chen

doi:10.1007/s11356-023-27822-4

Cited by 2 publications

(3 citation statements)

References 61 publications

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“…The main idea of the hybrid framework based on the decomposition method is to use decomposition methods to divide the time series into regular sub-modes and then use other models for forecasting. Sun, Wang, and Wei (2020), , Liu et al (2022), Yang et al (2022), and , Wang, Chudhery, et al (2023) demonstrated that decomposition techniques can effectively improve the forecasting accuracy.…”

Section: Combination Of Decomposition Methods and Forecasting Methodsmentioning

confidence: 99%

“…Interval forecasting is a trend modeling process in which the input is an interval-valued number, and the output result is also an interval-valued number. This is usually referred to using keywords such as interval time-series forecasting, interval forecasting, interval-valued data forecasting, and forecasting interval (Ak et al, Wang, Chudhery, et al, 2023;Xiong et al, 2014b;Xiong, Li, et al, 2015;Zhou et al, 2019).…”

Section: Interval Forecastingmentioning

confidence: 99%

“…Algorithm-based studies are focused on algorithmic improvement for enhancing forecasting performance, which can be achieved through the following techniques: reducing the volatility of the time series through decomposition techniques (Hu et al, 2015;Liu et al, 2022;Maia et al, 2008Maia et al, , 2011Xiong et al, 2014a;Zhang et al, 2020;, improving the robustness of the prediction results by combining prediction methods (Arroyo et al, 2011;Maia & Carvalho, 2019;Xiong et al, 2017;Wang, Chudhery, et al, 2023;Zhang et al, 2020), using the optimization algorithm to avoid the blindness of parameter selection (Jiang et al, 2023;Liu et al, 2019;Wang, Gao, et al, 2023;Xiong et al, 2014b;Zhou et al, 2019), or using a combination of these techniques.…”

Section: Introductionmentioning

confidence: 99%

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Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

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Interval time series forecasting can be used for forecasting special symbolic data comprising lower and upper bounds and plays an important role in handling the complexity, instability, and uncertainty of observed objects. The purpose of this research is to identify the most widely used definition of interval time series; classify existing research into mature research, current research focus, and research gaps within the defined framework; and recommend future directions for interval forecasting research. To achieve this goal, we have conducted a systematic literature review, comprising search strategy planning, screening mechanism determination, document analysis, and report generation. During the search strategy planning stage, eight literature search libraries are selected to obtain the most extensive studies (total of 525 targets). In the screening‐mechanism determination stage, through the inclusion and exclusion mechanism, the literature that is repetitive, of low‐relevance, and from other fields are discarded, and 125 studies are finally selected. In the document analysis stage, tag‐based methods and classification grids are selected to analyze the shortlisted studies. The results show that there are still numerous research gaps in interval time series forecasting, such as the establishment of hybrid models, application of multisource information, development and application of evaluation techniques, and expansion of application scenarios. In the report‐generation stage, the problems that have been solved and encountered in interval forecasting are summarized, and future research directions are proposed. Finally, the most significant contribution of this research is to provide an overview of interval time series forecasting for easy reference by researchers and to facilitate further research.

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