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Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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Computers, Materials & Continua 2024年第3期78卷 3529-3549页

作者： Ying Su Morgan C.Wang Shuai Liu Department of Statistics and Data Science University of Central FloridaOrlandoFL32816-2370USA School of Mathematics and Statistics Beijing Technology and Business UniversityBeijing100048China

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual *** manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series *** complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple *** surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series ***,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series ***,our research demonstrates that this innovative approach outperforms the traditional autoregressive integrated moving average(ARIMA)method in forecasting long-term univariate time *** is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing *** multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long ***,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural *** AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

关键词： Automated machine learning autoregressive integrated moving average neural networks time series analysis

A distributed real-time data prediction framework for large-scale time-series data using stream processing

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 145-165页

作者： Kehe Wu Yayun Zhu Quan Li Ziwei Wu School of Control and Computer Engineering North China Electric Power UniversityBeijingChina

Purpose-The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources,e.g.,sensor networks,securities exchange,electric power secondary system,***,the proposed framework should handle several difficult requirements including the management of gigantic data sources,the need for a fast self-adaptive algorithm,the relatively accurate prediction of multiple time series,and the real-time ***/methodology/approach-First,the autoregressive integrated moving average-based prediction algorithm is ***,the processing framework is designed,which includes a time-series data storage model based on the HBase,and a real-time distributed prediction platform based on ***,the work principle of this platform is ***,a proof-of-concept testbed is illustrated to verify the proposed ***-Several tests based on Power Grid monitoring data are provided for the proposed *** experimental results indicate that prediction data are basically consistent with actual data,processing efficiency is relatively high,and resources consumption is ***/value-This paper provides a distributed real-time data prediction framework for large-scale time-series data,which can exactly achieve the requirement of the effective management,prediction efficiency,accuracy,and high concurrency for massive data sources.

关键词： Prediction Real-time autoregressive integrated moving average Storm Stream processing Time series

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province,China

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Infectious Disease Modelling 2024年第1期9卷 224-233页

作者： Zixu Wang Wenyi Zhang Ting Wu Nianhong Lu Junyu He Junhu Wang Jixian Rao Yuan Gu Xianxian Cheng Yuexi Li Yong Qi Pest Control Department Huadong Research Institute for Medicine and BiotechniquesNanjingJiangsu province210002China Bengbu Medical College BengbuAnhui province233030China Chinese PLA Center for Disease Control and Prevention Beijing100071China Jinling Hospital Medical School of Nanjing UniversityNanjingJiangsu province210002China Ocean College Zhejiang UniversityZhoushan316021China Ocean Academy Zhejiang UniversityZhoushan316021China

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013–2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms autoregressive integrated moving average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.

关键词： Severe fever with thrombocytopenia syndrome Long short-term memory Prediction model autoregressive integrated moving average Prophet

A Soft Sensing Method for Operation Optimization of Coke Dry Quenching Process 36

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A Soft Sensing Method for Operation Optimization of Coke Dry...

Performance evaluation of series and parallel strategies for financial time series forecasting

第36届中国控制会议

作者： Jian-Guo Wang Zhi-Duo Cao Li-Lan Liu Bang-Hua Yang Shi-Wei Ma Min-Rui Fei Zhi-Fu Deng Yuan Yao Yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering “Tsing-Hua University” Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn’t follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote（NNG） variable selection algorithm with the autoregressive integrated moving average（ARIMA） model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.

关键词： Soft sensor Coke dry quenching Data-driven Variable selection autoregressive integrated moving average

来源： cnki会议评论

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Financial Innovation 2017年第1期3卷 357-380页

作者： Mehdi Khashei Zahra Hajirahimi Department of industrial and system engineering Isfahan University of TechnologyIsfahanIran

Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision *** its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied *** general,individual models are combined under two main strategies:series and *** it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid ***:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate ***:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock *** do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm ***:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.

关键词： Series and parallel combination strategies Multilayer perceptrons autoregressive integrated moving average Financial time series forecasting Stock markets

Forecasting the Monthly Reported Cases of Human Immunodeficiency Virus (HIV) at Minna Niger State, Nigeria

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Open Journal of Statistics 2020年第3期10卷 494-515页

作者： Nwanne Christiana Umunna Samuel Olayemi Olanrewaju Department of Statistics University of Abuja Abuja Nigeria

There has been a moderate increase in newly diagnosed HIV-infected Minna populace, which calls for serious attention. This study used time series data based on monthly HIV cases from January 2007 to December 2018 taken from the statistical data document on HIV prevalence recorded in General Hospital Minna, Niger State. The methodology employed to analyze the data is based on mathematical models of ARMA, ARIMA and SARIMA which were computed and diagnosed. From the results of parameter estimation of the models, ARMA(2, 1) model was the best model among the other ARMA models using information criteria (AIC). Diagnostic test was run on the ARMA(2, 1) model where the results show that the model was adequate and normally distributed using Box-Lung test and Q-Q plot respectively. Furthermore, ARIMA of first and second differences was estimated and ARIMA(1, 0, 1) was the best model from the result of the AIC and diagnostic test carried out which revealed that the model was adequate and normally distributed using Box-Lung and Q-Q plot respectively. Furthermore, the results obtained in the ARMA and ARIMA models were used to arrive at a combined model given as ARIMA(1, 0, 1) Human Immunodeficiency Virus autoregressive moving average autoregressive integrated moving average Seasonal autoregressive integrated moving average Forecasting