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At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also...
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Citations
... The OPCUA of the client [17][18][19][20]; • Data normalization methods [21][22][23]; • Algorithms for detecting and restoring any partially lost data [24][25][26][27]; • Methods of correlation analysis [28][29][30]; • Learning and forecasting using neural networks [31][32][33][34][35][36][37][38]. ...
AGVs are important elements of the Industry 4.0 automation process. The optimization of logistics transport in production environments depends on the economical use of battery power. In this study, we propose a novel deep neural network-based method and data mining for predicting segmented AGV battery voltage drop. The experiments were performed using data from the Formica 1 AGV of AIUT Ltd., Gliwice, Poland. The data were converted to a one-second resolution according to the OPCUA open standard. Pre-processing involved using an analysis of variance to detect any missing data. To do this, the standard deviation, variance, minimum and maximum values, range, linear deviation, and standard deviation were calculated for all of the permitted sigma values in one percent increments. Data with a sigma exceeding 1.5 were considered missing and replaced with a smoothed moving average. The correlation dependencies between the predicted signals were determined using the Pearson, Spearman, and Kendall correlation coefficients. Training, validation, and test sets were prepared by calculating additional parameters for each segment, including the count number, duration, delta voltage, quality, and initial segment voltage, which were classified into static and dynamic categories. The experiments were performed on the hidden layer using different numbers of neurons in order to select the best architecture. The length of the “time window” was also determined experimentally and was 12. The MAPE of the short-term forecast of seven segments and the medium-term forecast of nine segments were 0.09% and 0.18%, respectively. Each study duration was up to 1.96 min.
... This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
... By employing the out-ofsample forecasting framework, Rich et al. [12] found that, compared to those models generated from the univariate auto-regression, the coincident indexes can improve significantly tax base forecasts ether in statistic or in economic domains. Li [13] predicted financial time series data by combining the support vector machine model with the convolutional neural network model. In [14], Gan constructed a multiple regression model based on four explanatory variables, the fiscal expenditure, the GDP, the tax revenue and the fixed asset investment. ...
In this work, a novel optimal weighted combination Markov model (OWCMM) is proposed to forecast the public financial budget expenditure of Dongguan, China, from 2016 to 2020. The new model is constructed based on the optimal combination, which includes the fractional grey model, the Fourier function regression model and the autoregressive integrated moving average model (ARIMA), and modifies this optimal combination by the Markov model. The number of the optimal fractional order is determined by particle swarm optimization algorithm. One example is provided to verify the high fitting accuracy of the new model, the results show that the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the optimal weighted combination Markov model are smaller than that of the quadratic function model (QFM), the classical combinatorial model and its three sub-models, which proves the robustness of the optimal weighted combination Markov model. This work will provide a scientific basis and technical reference for the further research in finance field.
