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2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Ma, Haoxin Wang, Jiaqi Zhu, Mingkai School of Computer and Control Engineering Qiqihar University Qiqihar China School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798350369212

This study comprehensively analyzes the future production, sales and charging infrastructure expansion of new energy electric vehicles in China over the next decade, including production, sales and charging infrastructure expansion. The study uses the arima algorithm, combined with industry data analysis, to forecast the future development of the industry. In addition, the impact of new energy vehicles on the traditional automotive industry is analyzed through OLS regression analysis to gain insight into the challenges and opportunities brought about by the rise of new energy vehicles. This study provides valuable experience for the development of new energy vehicles and global sustainable development strategies. © 2024 IEEE.

关键词： arima algorithm New energy electric vehicles OLS regression analysis

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Forecasting model of total import and export based on arima algorithm optimized by BP neural network 3

Forecasting model of total import and export based on ARIMA ...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Zhang, Siyu Liu, Yichao Hua Lookeng Honors College Changzhou University Changzhou China China University of Geosciences School of Computing Wuhan China

ISBN: (纸本)9798350341546

Total import and export is an important economic indicator of a country, but because it is affected by many factors, the fluctuation pattern is often difficult to explore. If we can make a more accurate forecast of total import and export, it will be a guide for the policy adjustment of the country and enterprises. The problem of total import and export forecasting is a time series forecasting problem. The previous time series forecasting methods do not take into account the non-linear characteristics and other factors, so the forecasting accuracy of this problem is not high enough. In order to improve prediction accuracy, this paper designs an arima algorithm optimized by BP neural network. After the initial prediction by the arima algorithm, the BP neural network is trained by the extracted features, and the arima prediction is corrected by the trained BP neural network on the test set. After the design of the algorithm, real cases were predicted. The results show that the algorithm designed in this paper performs significantly better than the remaining two algorithms in all three error metrics. Compared to the arima algorithm, MSE, MAE, and MAPE improved by 9.4558 × 1015, 5.7376× 107 and 0.1051, respectively. Compared to the BP neural network, MSE, MAE, and MAPE improved by 4.1276 ×1015, 1.3469 × 107 and 0.0361, respectively. © 2023 IEEE.

关键词： arima algorithm BP neural network Non-linear Time series

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Disease Prediction Model based on Neural Network arima algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 333-339页

作者： Li, Kedong Jining Ctr Dis Control & Prevent Jining Peoples R China

Because the morbidity data of infectious diseases do not only have a single linear or nonlinear characteristic, but also have both linear and nonlinear characteristics, the combination model prediction method is often used to predict the morbidity of infectious diseases in recent years. Compared with the single model prediction analysis method, the combination model can combine the advantages of a single model to extract the effective information contained in the original time series more scientifically and fully. In the context of big data, for the medical field, massive medical data is complex, and the traditional manual data processing method has been unable to meet the current needs. With the help of the computer, data mining can discover new knowledge that is potentially useful and understandable by clearing, integrating, selecting, and transforming the original data. Using data mining, we can organize and reproduce the useful medical knowledge hidden in medical big data. In this paper, an arima-GRNN model is established;the fitting value and the corresponding time are used as the input of the neural network. The actual morbidity is used as the output to train the network and construct the arima-GRNN combined model. Due to the different information flow of BP neural network and neural network, this study also constructed arima-GRNN combined model and arima model, and compared the modeling effect and prediction performance of various models. The average absolute percentage error of the experimental results in this paper is less than 8.63%, and the average absolute percentage error is less than 5%. Compared with other models, it has a better prediction effect, higher accuracy, and more obvious advantages. In this paper, the prediction of disease is dynamic and continuous. It is of great significance for disease prevention and control to use monitoring data to study the epidemic trend and periodic change law, and to make a reasonable prediction.

关键词： Disease prevention and control trend prediction neural network combination model arima algorithm

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A Novel Approach for Accurate Stock Market Forecasting by Integrating arima and XGBoost

A Novel Approach for Accurate Stock Market Forecasting by In...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Somkunwar, Rachna K. Pimpalkar, Amit Srivastava, Vaibhav Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pune Pimpri India Nagpur India

ISBN: (纸本)9798350348460

In today's economy, the prediction and analysis of stock market data play a crucial role. Over the last decade, researchers have employed the neural network, an intelligent data mining technique, across various domains. Forecasting algorithms can be broadly classified into linear models and non-linear models. This paper employs the Autoregressive Integrated Moving Average (arima) linear model to develop a robust predictive framework for forecasting stock market trends. This study introduces an innovative method by combining the arima algorithm with XGBoost machine learning to create an advanced predictive model for stock market trends. By integrating temporal dependency capture from arima with the improved predictive capabilities of the machine-learning model, this approach aims to enhance the accuracy and effectiveness of stock market trend predictions. Leveraging the strengths of both methodologies, the hybrid model exhibits superior accuracy, surpassing standalone arima and XGBoost models. Training the model involves utilizing historical stock prices, trading volumes, and pertinent financial indicators. Evaluation will encompass the Google stock dataset to estimate the model's performance across various market conditions. By analyzing diverse historical stock market data, including prices, trading volumes, and financial indicators, the integrated model achieves a remarkable prediction accuracy of over 96.7%. The results underscore the efficacy of combining traditional time series analysis with advanced machine learning, positioning the arima-XGBoost hybrid model as a potent tool for precise and robust stock market trend forecasting. © 2024 IEEE.

关键词： arima algorithm Machine Learning Stock Market Time Series Analysis XGBoost algorithm

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Intelligent process control system for predicting operating conditions of slag grinding machines: a data mining approach for improved efficiency and energy savings

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MINERAL PROCESSING AND EXTRACTIVE METALLURGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY 2024年第1-2期133卷 7-20页

作者： Wan, Anping Du, Chenyu Chen, Ting He, Jiabo Al-Bukhaiti, Khalil Hangzhou City Univ Dept Mech Engn Hangzhou Peoples R China Zhejiang Univ Technol Dept Mech Engn Hangzhou Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu Peoples R China

This study develops an intelligent data-driven approach for optimising slag grinding systems. Slag grinding exhibits complex nonlinear dynamics that challenge control. The proposed system monitors key operating parameters to assess machine health and automate control adjustments. Operating data are collected, and features linked to health status are identified using data mining techniques. Cluster analysis categorises historical data into healthy/unhealthy modes to build a condition library. Real-time data are then evaluated against this library. A predictive model forecasts future trend. The system was implemented in an industrial slag mill. Results demonstrated reduced vibration and energy use versus manual control. Validation confirmed improved accuracy in predicting mill responses. Significant energy savings were achieved annually through optimised control. The system enhances safety by automating adjustments while minimising costs and environmental impacts. Data-driven strategies overcome the limitations of traditional methods, representing an advance for intelligent management of industrial processes. Benefits were confirmed through rigorous factory implementation and performance monitoring.

关键词： Slag grinding energy savings intelligent process feature extraction arima algorithm

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Research on Gas Concentration Prediction Based on the arima-LSTM Combination Model

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PROCESSES 2023年第1期11卷 174-174页

作者： Li, Chuan Fang, Xinqiu Yan, Zhenguo Huang, Yuxin Liang, Minfu China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China Shaanxi Yanchang Petr & Min Ltd Co Xian 710065 Peoples R China Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Peoples R China China Univ Min & Technol Res Ctr Intelligent Min Xuzhou 221116 Peoples R China

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an arima-LSTM combined forecasting model based on the autoregressive integrated moving average (arima) model and the long short-term memory (LSTM) recurrent neural network. In the arima-LSTM model, the arima model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R-2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined arima-LSTM model are R-2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.

关键词： gas prediction arima algorithm LSTM algorithm data fitting

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Data-Driven Traffic Accident Analysis and Prediction Using Machine Learning Models: A Case Study of Philadelphia City 7

Data-Driven Traffic Accident Analysis and Prediction Using M...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Lyu, Chengxuan Penn State Univ Sch Elect Engn & Comp Sci 201 Old Main University Pk PA 16802 USA

ISBN: (纸本)9781510674479

Traffic safety problem has been highly concerned by people all over the world. Predicting potential traffic accidents can help to improve traffic facilities or emergency system, and give people alerts to potential dangers. As a random event, the occurrence of traffic accidents has obvious seasonal and spatial characteristics. The accuracy of traffic accident prediction can be improved by combining the seasonal and spatial characteristics of traffic accidents. In the study, we selected Philadelphia car accident databases during 2008 to 2012 on Opendata Philly. On the basis of data grouping preprocessing, we tested three machine learning algorithms to predict the count of accidents. The results indicate that LSTM(Long Short-Term Memory) algorithm has the best performance relatively, and XGBoost (eXtreme Gradient Boosting) model does not perform better than arima(Autoregressive Integrated Moving Average model. In addition, we identify the important features of traffic accidents in Philadelphia city: the count of accidents on the same road segment, traffic control device, automobile type, roadway surface condition, weather condition. The datasets in this study has obvious spatial characteristics also, and the occurrence of accidents is closely related to the accident segment.

关键词： Traffic accidents Data analysis Machine learning arima algorithm XGBoost algorithm LSTM algorithm

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Research on Vegetable Pricing Strategy Based on Simplified Single-Factor Optimization Model

Research on Vegetable Pricing Strategy Based on Simplified S...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Kuang, Xi International School of Business and Finance Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350309614

The price of vegetables in the supermarket is affected by many factors such as the order quantity of the category, the single variety provided by the supplier, the quantity, the loss rate, and the change in customer demand. Most of the factors have a strong correlation with time, and some of them change periodically with time. Models need to be built to solve the relevant decision problems. In this paper, the problem is mainly divided into two parts: 1) to solve the relationship between the total sales of each vegetable category and the cost-plus pricing. 2) figure out the pricing strategy that maximizes profit. In this paper, in order to explore the relationship between the total sales volume and cost-plus pricing, the first two quantitative analyses the functional relationship between the two. Then, in order to construct the constraints of the optimization model, the arima algorithm is used to predict the time series data. In the end, the multi-factor model is simplified into a single-factor optimization model to optimize the pricing strategy of vegetables, and the optimization model is solved based on the differential evolution algorithm. © 2023 IEEE.

关键词： arima algorithm Cost plus pricing Maximizes profit Single-factor optimization model

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Predicting Incidence Time Series Based on LSTM Recurrent Neural Network and Key Feature Analysis Based on Random Forest algorithm

Predicting Incidence Time Series Based on LSTM Recurrent Neu...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Du, Jinqi Yang, Yujie Geng, Shuai Liu, Xin Wang, Yafei Shenyang University of Chemical Technology School of Mechanical and Power Engineering Shenyang China

ISBN: (纸本)9798331527624

This paper presents a study on the prediction of the incidence rate time series and the analysis of key features using the LSTM recurrent neural network and the random forest algorithm. Specifically, the study focuses on forecasting the incidence rate for the years 2021-2024 using the arima algorithm and the LSTM recurrent neural network. The arima prediction revealed that the number of diagnosed cases of pulmonary tuberculosis in Liaoning Province reached its lowest point in 2020 over the past decade, showing a 2.4/100,000 decrease in the incidence rate compared to 2019. Furthermore, the LSTM recurrent neural network predicted a continuous decrease in the overall number of cases, indicating a declining trend in the incidence rate. Finally, the random forest algorithm was employed to extract the key features influencing the incidence rate. © 2024 IEEE.

关键词： arima algorithm LSTM recurrent neural network Random forest algorithm

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Forecasting Uncertainty Parameters of Virtual Power Plants Using Decision Tree algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第16期51卷 1756-1769页

作者： Krishna, Raji Sathish, Hemamalini Zhou, Ning Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India SUNY Binghamton ECE Dept New York NY USA

Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (arima) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R-2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and arima algorithms.

关键词： forecasting uncertainty parameters renewable energy sources distributed generators machine learning decision tree algorithm ANN algorithm arima algorithm statistical metrics

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