A Wine Consumption Prediction Model Based on L-DAGLSSVM

Xiao, Wang; Lu, Sijie; Zhou, Zhijian

doi:10.1007/978-3-319-72745-5_35

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…So far, scholars have conducted extensive researches on time series forecasting with three major methods: traditional econometric model, artificial intelligence (AI) prediction and combining models. Traditional econometric models, such as ARMA model, ARIMA model, GARCH model and vector autoregression model (VAR), have much limitations in dealing with nonlinear and non-stationary time series [ 26 ]. To address these issues, AI uses machine learning technology to train historical data, to realize higher prediction accuracy in nonlinear time series data.…”

Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in stock market based on EEMD-LSTM

Ran,

Tang,

Chen

et al. 2024

PLoS ONE

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With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

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Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in stock market based on EEMD-LSTM

Ran,

Tang,

Chen

et al. 2024

PLoS ONE

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“…Finally, LSSVM classification function is obtained by Similar to SVM, LSSVM is also confronted with the choice of kernel function (Wang et al, 2018;Xiao-ru et al, 2018). Different kernel…”

Section: Least Square Support Vector Machine (Lssvm)mentioning

confidence: 99%

Sensory evaluation and prediction of bulk wine by physicochemical indicators based on PCA‐PSO‐LSSVM method

Zhang

et al. 2022

Food Processing Preservation

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A Wine Consumption Prediction Model Based on L-DAGLSSVM

Cited by 2 publications

References 4 publications

Early warning of systemic risk in stock market based on EEMD-LSTM

Early warning of systemic risk in stock market based on EEMD-LSTM

Sensory evaluation and prediction of bulk wine by physicochemical indicators based on PCA‐PSO‐LSSVM method

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