Yuke Cheng's research while affiliated with Changzhou University and other places
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Publications (4)
This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation (AGO), least-square support vector regression (LSSVR), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal tren...
Considering the weakness in the discrete grey seasonal model, a new grey seasonal model is put forward by introducing a time trends item. Moreover, some properties of this proposed model are deduced, such as the unbiased feature, to provide more information to perceive this model. Subsequently, four time series concerning the quarterly and monthly...
Foreknowledge of the air quality indicators (i.e. AQI, PM2.5, PM10, SO2, CO, NO2, and O3) provides decision-makers a possibility for building an early-warning system and tailoring related policies and plans accordingly so as to reduce the negative influences of these pollutants. However, accurate forecasts are hardly obtained because strong seasona...
Citations
... A hybrid model developed by Zhou et al. combines the grey model with a machine learning model in order to predict seasonal trends in energy consumption. The results of experiments indicate that the proposed model is more accurate than its predecessors [10] . ...
... The grey model is a commonly used mathematical modeling method for studying the prediction of uncertain systems. The existing seasonal grey prediction models include the seasonal grey model (SGM (1,1)) [19], discrete grey seasonal model (DGSM (1,1)) [2], data grouping seasonal time model (DGSTM (1,1)) [20], average weakening buffer operators-data grouping grey model (AWBO-DGGM (1,1)) [21], particle swarm optimized fractional-order-accumulation discrete grey seasonal model (PFSM(1,1)) [22], data restacking-seasonal factor grey model (DR-SFGM) [6], seasonal division-based grey seasonal variation index (OSGSVI) [23], and weighted average weakening buffer operator-fractional order accumulation seasonal grouping grey model (WAWBO-FSGGM (1,1)) [24], which are widely used in many fields. In addition, due to their broad applicability, small sample size requirement, and high accuracy, grey prediction models are widely used after continuous optimization and improvement. ...
... Next, we want to point out that, over time, predictive models tend to become more accurate but also more complex, indeed, based on the experience of previous model developments. Zhou et al. (2020), for example, in his model made measurements at different seasons in four cities in the Yangtze River Delta with five prevalent forecasting tools, including SFGM (Seasonal Fractional-order Grey Model), SGM (Seasonal Grey Model), LSSVM (Least Squares Model) and LSSVM (Least Squares Squared Model), and LSSVM (Least Squares Squared Model), LSSVM (Least Squares Support Vector Machine), SARIMA (Seasonal Auto-regress Integrated Moving Average) and BPNN (Back Propagation Neural Network), with a considerable improvement of the prediction accuracy of seasonal air quality changes (Zhou et al. 2020). By increasing and thus enriching the data available for modeling, Pinto et al. (2020) proposed introducing data on traffic patterns (speed, intensity) and emissions generated by vehicle emission models (Pinto et al. 2020). ...
