scholarly journals Analysis of public opinion evolution of COVID-19 based on LDA-ARMA hybrid model

2021 ◽  
Author(s):  
Muni Zhuang ◽  
Yong Li ◽  
Xu Tan ◽  
Lining Xing ◽  
Xin Lu
Keyword(s):  
Public Opinion ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Moving Average ◽  
Classification Model ◽  
Average Error ◽  
Autoregressive Moving Average Model ◽  
Treatment Measures ◽  
Moving Average Model ◽  
The Government

AbstractThe aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

scholarly journals Analysis and prediction of polar motion using MSSA method

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Polar Motion ◽  
Rotation Axis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Rotational Axis ◽  
Autoregressive Moving Average Model ◽  
System Service ◽  
Moving Average Model ◽  
Polar Motion Prediction

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

Active, passive, and motor imagery paradigms: component analysis to assess neurovascular coupling

2013 ◽  
Vol 114 (10) ◽  
pp. 1406-1412 ◽  
Author(s):  
Angela S. M. Salinet ◽  
Thompson G. Robinson ◽  
Ronney B. Panerai
Keyword(s):  
Motor Imagery ◽  
Moving Average ◽  
Neurovascular Coupling ◽  
Neural Activation ◽  
Autoregressive Moving Average Model ◽  
Arterial Blood ◽  
Moving Average Model ◽  
End Tidal ◽  
The Right ◽  
Cognitive Paradigms

The association between neural activity and cerebral blood flow (CBF) has been used to assess neurovascular coupling (NVC) in health and diseases states, but little attention has been given to the contribution of simultaneous changes in peripheral covariates. We used an innovative approach to assess the contributions of arterial blood pressure (BP), PaCO2, and the stimulus itself to changes in CBF velocities (CBFv) during active (MA), passive (MP), and motor imagery (MI) paradigms. Continuous recordings of CBFv, beat-to-beat BP, heart rate, and breath-by-breath end-tidal CO2 (EtCO2) were performed in 17 right-handed subjects before, during, and after motor-cognitive paradigms performed with the right arm. A multivariate autoregressive-moving average model was used to calculate the separate contributions of BP, EtCO2, and the neural activation stimulus (represented by a metronome on-off signal) to the CBFv response during paradigms. Differences were found in the bilateral CBFv responses to MI compared with MA and MP, due to the contributions of stimulation ( P < 0.05). BP was the dominant contributor to the initial peaked CBFv response in all paradigms with no significant differences between paradigms, while the contribution of the stimulus explained the plateau phase and extended duration of the CBFv responses. Separating the neural activation contribution from the influences of other covariates, it was possible to detect differences between three paradigms often used to assess disease-related NVC. Apparently similar CBFv responses to different motor-cognitive paradigms can be misleading due to the contributions from peripheral covariates and could lead to inaccurate assessment of NVC, particularly during MI.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

2021 ◽  
pp. 0734242X2110614
Author(s):  
AKM Mohsin ◽  
Lei Hongzhen ◽  
Mohammed Masum Iqbal ◽  
Zahir Rayhan Salim ◽  
Alamgir Hossain ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Production Capacity ◽  
Waste Recycling ◽  
Support Vector ◽  
Waste Recovery ◽  
Decomposition Procedure ◽  
Moving Average Model ◽  
The Government ◽  
Seasonal Data

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

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