An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting

Zhu, Bangzhu; Zhang, Jingyi; Wan, Chunzhuo; Chevallier, Julien; Wang, Ping

doi:10.1002/for.2916

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…In complex real‐world environments, datasets often suffer from data imbalance, which ultimately affects the predictive performance (Zhu, Zhang, et al, 2022). Furthermore, with the growing concerns regarding cybersecurity and malicious data tampering, an emerging topic is the development of robust predictive models (Luo et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

Interval time series forecasting: A systematic literature review

Wang,

Gurmani,

Tao

et al. 2023

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…These networks advance predictive capabilities by initially furnishing a forecast grounded in NAR networks, followed by posteriori corrections utilizing SVR to rectify any discrepancies in the prediction of observed variables. Although some preliminary research has explored similar concepts [51][52][53], no publication to date has specifically employed this algorithm on time series forecasting. To establish the generalizability of our findings, this work will compare the performance of the new model with others.…”

Section: Introductionmentioning

confidence: 99%

Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks

Borrero,

Mariscal

2023

Algorithms

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Efforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning algorithms, has been explored to achieve accurate forecasts. The hybrid ARIMA-SVR model has garnered attention due to its fusion of a foundational linear model with error correction capabilities. However, its use is limited to stationary time series data, posing a significant challenge. To overcome these limitations and drive progress, we propose the innovative NAR–SVR hybrid method. Unlike its predecessor, this approach breaks free from stationarity and linearity constraints, leading to improved model performance solely through historical data exploitation. This advancement significantly reduces the time and computational resources needed for precise predictions, a critical factor in univariate economic time series forecasting. We apply the NAR–SVR hybrid model in three scenarios: Spanish berry daily yield data from 2018 to 2021, daily COVID-19 cases in three countries during 2020, and the daily Bitcoin price time series from 2015 to 2020. Through extensive comparative analyses with other time series prediction models, our results substantiate that our novel approach consistently outperforms its counterparts. By transcending stationarity and linearity limitations, our hybrid methodology establishes a new paradigm for univariate time series forecasting, revolutionizing the field and enhancing predictive capabilities across various domains as highlighted in this study.

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Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

Zhang,

Song,

Peng

et al. 2024

Journal of Forecasting

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Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.

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An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting

Cited by 10 publications

References 27 publications

Interval time series forecasting: A systematic literature review

Interval time series forecasting: A systematic literature review

Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks

Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model

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