scholarly journals EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Menglong Wu ◽  
Yicheng Ye ◽  
Nanyan Hu ◽  
Qihu Wang ◽  
Huimin Jiang ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Signal Sequence ◽  
Low Frequency ◽  
Arma Model ◽  
Short Term ◽  
Svm Model ◽  
Safety Production ◽  
Mining Safety ◽  
Production Situation

In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

scholarly journals Uncertainty Prediction of Mining Safety Production Situation

2021 ◽  
Author(s):  
Menglong WU ◽  
Yicheng YE ◽  
Nanyan HU ◽  
Qihu WANG ◽  
Wenkan TAN
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Intrinsic Mode Function ◽  
Optimal Time ◽  
Uncertain Reasoning ◽  
Uncertainty Prediction ◽  
Mode Function ◽  
Safety Production ◽  
Mining Safety ◽  
Production Situation

Abstract In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R and Up three granular parameter sequences, according to the triangular fuzzy number; Then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; Finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R and Up sequences were 9.472 %, 16.671 % and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.

scholarly journals Research on Short-term Ionospheric Prediction Combining with EOF and ARIMA Model Over Guangxi Area

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1147-1153
Author(s):  
C. Li ◽  
H. Peng ◽  
L. K. Huang ◽  
L. L. Liu ◽  
S. F. Xie
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Prediction Accuracy ◽  
Arima Model ◽  
Stationary Time Series ◽  
Short Term ◽  
Equatorial Anomaly ◽  
Average Standard Deviation ◽  
Stationary Time ◽  
Low Latitudes

Abstract. According to the empirical orthogonal function (EOF), the non-stationary time series data are decomposed into time function and space function, so this mathematical method can simplify the non-stationary time series and eliminate redundant information, thus it performs well in non-stationary time series analysis. The ionospheric Vertical Total Electron Content (VTEC) is a non-stationary time series, which has non-stationary and seasonal variation and the activity of VTEC is more active in low latitudes. Guangxi is located in the middle and low latitudes of the Northern Hemisphere with abundant sunshine in summer and autumn. The energy released by solar radiation makes the ionospheric activity in this region more complex than that in the high latitudes. However, no expert or scholar has used EOF analysis method to conduct a comprehensive study of the low latitudes. The International GNSS Service (IGS) provided by high precision Global Ionospheric Maps (GIM) center in Guangxi are used in the modeling data, the GIM data of the first 10 days of different seasons are decomposed by EOF, and then the time function is predicted by ARIMA model. VTEC values for the next five days are obtained through reconstruction, and relative accuracy and standard deviation are used as accuracy evaluation criteria. The results of EOF-ARIMA model are compared with those of ARIMA model, and the prediction accuracy of EOF-ARIMA model at the equatorial anomaly is analyzed in order to explore the reliability of the model in the more complex region of ionospheric activity. The results show that the average relative precision of EOF-ARIMA model is 84.0, the average standard deviation is 7.45TECu, the average relative precision of ARIMA model is 81.5, the average standard deviation is 8.29TECu, and the precision of EOF-ARIMA model is higher than that of ARIMA model.; There is no significant seasonal difference in the prediction accuracy of EOF-ARIMA model, and the prediction accuracy of ARIMA model in autumn is lower than that of other seasons, which indicates that the prediction results of EOF-ARIMA model are more reliable; The prediction accuracy of the EOF-ARIMA model at the equatorial anomaly is not affected, and it is consistent with the accuracy of the high latitude area in Guangxi. It is shown that the EOF-ARIMA model has high accuracy and stability in the short-term ionospheric prediction in Guangxi at low latitudes of China, and provides a new and reliable method for ionospheric prediction at low latitudes.

scholarly journals Water quality prediction analysis of Qingyi River based on time series

2019 ◽  
Vol 118 ◽  
pp. 03005
Author(s):  
Yulan Luo ◽  
Qingsong Chen ◽  
Ying Liu ◽  
Xiaohui Xie ◽  
Qianying Du
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Prediction Accuracy ◽  
Drainage Basin ◽  
Oxygen Demand ◽  
Arma Model ◽  
Ammonia Nitrogen ◽  
Series Method ◽  
Water Quality Prediction ◽  
Time Series Method

According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality.

Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women

2018 ◽  
Vol 63 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Sina Reulecke ◽  
Sonia Charleston-Villalobos ◽  
Andreas Voss ◽  
Ramón González-Camarena ◽  
Jesús González-Hermosillo ◽  
...  
Keyword(s):  
Time Series ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Short Term ◽  
Auto Correlation ◽  
Complete Test ◽  
Auto Correlation Function ◽  
Head Up Tilt ◽  
Cardiovascular Autonomic Regulation ◽  
Sustained Increase

AbstractLinear dynamic analysis of cardiovascular and respiratory time series was performed in healthy subjects with respect to gender by shifted short-term segments throughout a head-up tilt (HUT) test. Beat-to-beat intervals (BBI), systolic (SYS) and diastolic (DIA) blood pressure and respiratory interval (RESP) time series were acquired in 14 men and 15 women. In time domain (TD), the descending slope of the auto-correlation function (ACF) (BBI_a31cor) was more pronounced in women than in men (p<0.05) during the HUT test and considerably steeper (p<0.01) at the end of orthostatic phase (OP). The index SYS_meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP_sdNN) were found in women (p<0.05), during 10–20 min after tilt-up. In frequency domain (FD), during baseline (BL), BBI-normalized low frequency (BBI_LFN) and BBI_LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI_HFN) was significantly higher in women. These differences were highly significant from the first 5 min after tilt-up (p<0.01) and highly significant (p<0.001) during 10–14 min of OP. Findings revealed that men showed instantaneously a pronounced and sustained increase in sympathetic activity to compensate orthostatism. In women, sympathetic activity was just increased slightly with delayed onset without considerably affecting sympatho-vagal balance.

scholarly journals Characterizing and Forecasting Climate Indices Using Time Series Models

2021 ◽  
Author(s):  
Taesam Lee ◽  
Taha B.M.J. Ouarda ◽  
Ousmane Seidou
Keyword(s):  
Time Series ◽  
Great Lakes ◽  
Southern Oscillation ◽  
Moving Average ◽  
Low Frequency ◽  
Arma Model ◽  
Water Levels ◽  
Climate Indices ◽  
Autoregressive Moving Average ◽  
Time Series Models

Abstract The objective of the current study is to present a comparison of techniques for the forecasting of low frequency climate oscillation indices with a focus on the Great Lakes system. A number of time series models have been tested including the traditional Autoregressive Moving Average (ARMA) model, Dynamic Linear model (DLM), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, as well as the nonstationary oscillation resampling (NSOR) technique. These models were used to forecast the monthly El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) indices which show the most significant teleconnection with the net basin supply (NBS) of the Great Lakes system from a preliminary study. The overall objective is to predict future water levels, ice extent, and temperature, for planning and decision making purposes. The results showed that the DLM and GARCH models are superior for forecasting the monthly ENSO index, while the forecasted values from the traditional ARMA model presented a good agreement with the observed values within a short lead time ahead for the monthly PDO index.

A NOVEL HYBRID FRACTAL INTERPOLATION-SVM MODEL FOR FORECASTING STOCK PRICE INDEXES

Fractals ◽  
2019 ◽  
Vol 27 (04) ◽  
pp. 1950055 ◽  
Author(s):  
HONG-YONG WANG ◽  
HONG LI ◽  
JIN-YE SHEN
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Stock Index ◽  
Support Vector ◽  
Fractal Interpolation ◽  
Interpolation Model ◽  
Svm Model ◽  
Price Indexes

Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model are integrated to predict the every 5-min high frequency index data of Shanghai Composite Index. The experimental results show that the hybrid model is suitable for forecasting the stock index time series with fractal characteristics. In addition, a comparison of the prediction accuracy is carried out among the hybrid model and other three commonly used models. The results show that the prediction performance of the hybrid model is superior to that of other three models.

Short-Term Wind Speed Combination Prediction Model of Neural Network and Time Series

2012 ◽  
Vol 608-609 ◽  
pp. 764-769
Author(s):  
Hao Zheng ◽  
Jian Yan Tian ◽  
Fang Wang ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Prediction Model ◽  
Wind Direction ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Input Variables

This paper uses neural network combined with time series to establish rolling neural network model to predict short-term wind speed in the wind farm. In order to improve wind speed prediction accuracy, this paper analyzes effects of wind direction on wind speed by gray correlation analysis and obtains the correlation coefficient between wind speed at next moment and current wind direction is the largest by calculating. Then wind direction at current moment, historical wind speed and residuals which determined by time series are used as input variables to establish wind prediction model with rolling BP neural network. The simulation results show that neural network combined with time series which considers wind direction could improve the prediction accuracy when wind speed fluctuation is large.

scholarly journals Short-term Load Forecasting Model for Microgrid Based on HSA-SVM

2018 ◽  
Vol 173 ◽  
pp. 01007
Author(s):  
Han Aoyang ◽  
Yu Litao ◽  
An Shuhuai ◽  
Zhang Zhisheng
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Harmony Search ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Search Optimization ◽  
Svm Model ◽  
Short Term Load Forecasting

Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.

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