Early box office prediction in China’s film market based on a stacking fusion model

Liao, Yi; Peng, Yuxuan; Shi, Shengjun; Shi, Victor; Yu, Xiaohong

doi:10.1007/s10479-020-03804-4

Cited by 15 publications

(6 citation statements)

References 25 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…:(0123456789) Compared with a single model, the stacking framework combines the advantages of multiple models with enhanced resistance to interference and generalization. All base learners use the five-fold cross-validation method, effectively preventing model overfitting 30,31,38 .…”

Section: Stacking Model Fusion (Sfm)mentioning

confidence: 99%

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

You,

Zhou,

Zhang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R2) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.

show abstract

Section: Stacking Model Fusion (Sfm)mentioning

confidence: 99%

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

You,

Zhou,

Zhang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In view of the fact that no single model can meet the requirements of training performance and stability well, this paper attempts to use the Stacking to synthesize the advantages of various boosting models [33]. Moreover, combining it with the LR regression model enables the fusion model to have strong discrimination and stability, and does not require too frequent iterations on the basis of achieving good results.…”

Section: Power Demand Forecasting Model Based On Stackingmentioning

confidence: 99%

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Zhang

Duan

et al. 2022

Mathematics

View full text Add to dashboard Cite

With the continuous development of economy and society, power demand forecasting has become an important task of the power industry. Accurate power demand forecasting can promote the operation and development of the power supply industry. However, since power consumption is affected by a number of factors, it is difficult to accurately predict the power demand data. With the accumulation of data in the power industry, machine learning technology has shown great potential in power demand forecasting. In this study, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are integrated by stacking to build an XLG-LR fusion model to predict power demand. Firstly, preprocessing was carried out on 13 months of electricity and meteorological data. Next, the hyperparameters of each model were adjusted and optimized. Secondly, based on the optimal hyperparameter configuration, a prediction model was built using the training set (70% of the data). Finally, the test set (30% of the data) was used to evaluate the performance of each model. Mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness-of-fit coefficient (R^2) were utilized to analyze each model at different lengths of time, including their seasonal, weekly, and monthly forecast effect. Furthermore, the proposed fusion model was compared with other neural network models such as the GRU, LSTM and TCN models. The results showed that the XLG-LR model achieved the best prediction results at different time lengths, and at the same time consumed the least time compared to the neural network model. This method can provide a more reliable reference for the operation and dispatch of power enterprises and future power construction and planning.

show abstract

“…us, these films have to be exhibited on public holidays or after work when more youngers are available on the market [10]. Second, the publicity and distribution malfunctions without a detailed analysis of the market demand and the absence of Internet technology.…”

Section: Marketing Status Of Bj Film Companymentioning

confidence: 99%

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the growing scale of the domestic film industry, the output of domestic films is also increasing. The design and execution problems in ticket marketing strategies adversely affect the revenue of domestic films. This paper aims to propose solutions to optimize film companies’ marketing strategies by analyzing the marketing environment and current situation to increase the income of domestic films. Firstly, the current situation of BJ’s marketing is analyzed, and the main problems are clarified. Secondly, the necessity and feasibility of applying blockchain technology to the operation and management of film companies are introduced. New marketing strategies and safeguards have been developed by analyzing the target market and optimizing the program. A quantitative method is used to predict a new movie’s box office in its premiere month. The proposed method introduces three factors affecting the box office: new film positioning, film marketing, and film prerating, as input variables. Besides, a Recurrent Neural Network (RNN) is implemented to predict the monthly box office of the premiere of new films. The results show that the predicted monthly box office of the movie adopting the optimized marketing strategy is 1,451,718.6 CNY, which is smaller than the target box office of 1414029.8. The Mean Absolute Error (MAE) is only 0.026. The proposed model’s Root Mean Square Error (RMSE) is 0.45 for the long-term prediction of a single movie price. The MAE is 0.106, and the accuracy is 0.80. The model proposed beats the unimproved Long Short-Term Memory (LSTM) model and the Autoregressive Moving Average (ARMA) model. This paper provides a reference for optimizing the film company’s marketing system.

show abstract

Early box office prediction in China’s film market based on a stacking fusion model

Cited by 15 publications

References 25 publications

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Contact Info

Product

Resources

About