Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

Stochastic Modeling in Stock Market Trend Prediction

n Modern centuries a lot of predicting techniques take been proposed and applied for the stock market movement prediction. In this paper, the pattern examinations of the financial exchange forecast are introduced by utilizing Hidden Markov Model with the one day distinction in close incentive for a particular period. The likelihood esteems π gives the pattern level of the stock costs which is determined for all the notice arrangement and stowed away successions. It supports for decision makers to make decisions in case of indecision on the basis of the proportion of probability values found from the steady state probability distribution.

Long-term Trend in Mean Density of Antarctic Krill (Euphausia superba) Uncertain

Two recent attempts to model the long-term trend in mean density of Antarctic krill in the southwestern sector of the Atlantic using the KRILLBASE dataset using different statistical methods as well as inclusion versus exclusion of data from “non-scientific” nets have resulted in disparate conclusions. The approach that used a linear mixed model (LMM) fitted to the log of mean density, after standardisation was applied to individual net hauls and with means calculated for 12 spatial strata by years between 1976 and 2016, gave a highly statistically significant linear “regional” decline north of 60oS and, to a lesser degree, south of this latitude. The alternative approach that used a ”hurdle” model fitted to the individual net haul data, excluded regional stratification, and excluded non-scientific nets failed to detect an overall significant decline. The method of modelling log transformed means was reappraised and corrected by applying a meta-analytic LMM approach. Additionally, nonlinear smooths in year by region and a smooth in mean “climatological temperature” were included in the LMM. This model showed on average a mostly consistent decline north of 60oS, however, neither trend was significantly different from a no-trend prediction with the trend north of 60oS highly uncertain. Uncertainty of predictions resulted in only weak power to detect a substantial decline of the order of 70% between 1985 and 2005. These model-based inferences neither strongly support nor reject a general hypothesis that there has been a dramatic decline in density of Antarctic krill in the Southwest Atlantic over this period.

Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language

This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. With the development of computer science, the author now uses many computer science techniques to make more accurate predictions of stock prices. In this project, the author uses machine learning in R Studio to predict the prices of 35 stocks traded on the New York Stock Exchange and to study the interaction between the prices of four indices in different countries. Further, it is proposed to find the link between stocks and indices in different countries and then use the predictions to optimize the portfolio of these stocks. To complete this project, the author used Linear Regression, LASSO, Regression Trees, Bagging, Random Forest, and Boosted Trees to perform the analysis. The experimental results show that the MRDL deep multiple regression model proposed in this paper predicts the closing price trend of stocks with a mean square error interval [0.0043, 0.0821]. Additionally, 80% of the proposed DMISV, KDJSV, MACDV, and DKB stock buying and selling strategies have a return greater than 10%. The experimental results validate the effectiveness of the proposed buying and selling strategies and stock price trend prediction methods in this paper. Compared with other algorithms, the accuracy of the algorithm in this study is increased by 15%, and the efficiency of prediction is increased by 25%.

Economic Development Trend Prediction Model Based on Unsupervised Learning in the Internet of Things Environment

In order to explore the economic development trend under the environment of the Internet of Things, this paper improves the chaotic algorithm of the Internet of Things and constructs an economic development trend analysis system based on big data technology. Moreover, this paper analyzes the actual situation of big data processing data and conducts research on economic data analysis process. In addition, this paper conducts effective research on the various modules of functional analysis, obtains the system functional architecture, constructs the system functional structure based on the actual situation, and analyzes the operating process of the system. Finally, this paper designs a simulation test based on actual data. The experimental research results show that the system model proposed in this paper has a good performance in the forecast of economic development trends, and the system can be used for forecasting in subsequent economic development forecasts.