scholarly journals Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262009
Author(s):  
Rui Zhang ◽  
Hejia Song ◽  
Qiulan Chen ◽  
Yu Wang ◽  
Songwang Wang ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Time Scales ◽  
Hemorrhagic Fever ◽  
Early Warning Systems ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Forecasting Models ◽  
Best Fitting ◽  
Different Time Scales ◽  
Better Than

Objectives This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results ARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Comparison of ARIMA and LSTM for Prediction of Hemorrhagic Fever at Different Time Scales in China

2021 ◽  
Author(s):  
Rui Zhang ◽  
Qiulan Chen ◽  
Qiang Chen ◽  
Yujie Meng ◽  
Huan Zheng ◽  
...  
Keyword(s):  
Time Scales ◽  
Arima Model ◽  
Hemorrhagic Fever ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecasting Performance ◽  
Forecast Models ◽  
Monthly Incidence ◽  
Different Time Scales ◽  
Better Than

Abstract ObjectivesThis study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China.MethodsARIMA and LSTM model were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecast, were used to predict the incidence in 2019 to identify its stability and availability. ResultsARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) was selected as the best fitted ARIMA model for monthly, weekly and daily incidence series respectively. The model with 64 neurons and SGDM for monthly incidence, 8 neurons and Adam for weekly incidence, and 64 neurons and RMSprop for daily incidence were selected as the best fitted LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecast in 2019 were lower than those of the direct forecast models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling models.ConclusionsBoth ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever meanwhile different models might be more suitable for the incidence prediction at different time scales.

scholarly journals Kinerja Algoritma Optimasi Root-Mean-Square Propagation dan Stochastic Gradient Descent pada Klasifikasi Pneumonia Covid-19 Menggunakan CNN

2021 ◽  
Vol 7 (3) ◽  
pp. 420
Author(s):  
Budi Nugroho ◽  
Eva Yulia Puspaningrum ◽  
M. Syahrul Munir
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Root Mean Square ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
X Ray

Penelitian ini berkaitan dengan proses klasifikasi Pneumonia Covid-19 (radang paru-paru atau pneumonia yang disebabkan oleh virus corona SARS-CoV-2) dari citra hasil foto rontgen / x-ray paru-paru dengan menggunakan pendekatan pembelajaran mesin. Klasifikasi dilakukan untuk menentukan apakah kondisi paru-paru seseorang mengalami Pneumonia Covid-19, Pneumonia biasa, atau Normal / Sehat. Untuk menghasilkan kinerja klasifikasi yang lebih baik, proses optimasi seringkali digunakan pada tahap pelatihan data. Banyak teknik yang digunakan untuk melakukan optimasi tersebut, diantaranya adalah algoritma Root-Mean-Square Propagation (RMSprop) dan Stochastic Gradient Descent (SGD). Pada penelitian ini, pengujian dilakukan terhadap kedua metode tersebut untuk mengetahui kinerjanya pada klasifikasi Pneumonia Covid-19. Metode klasifikasi menggunakan Convolutional Neural Network (CNN) yang menerapkan 5 layer konvolusi dengan nilai filter 16, 32, 64, 128, dan 256. Proses pelatihan menggunakan 3.900 citra yang terdiri atas 1.300 citra pneumonia covid-19, 1.300 citra pneumonia, dan 1.300 citra normal. Sedangkan proses validasi menggunakan 450 citra dan proses pengujian mengunakan 225 citra. Berdasarkan uji coba yang telah dilakukan, implementasi algoritma optimasi RMSprop menghasilkan akurasi 87,99%, presisi 0,88, recall 0,86, dan f1 score 0,87. Sedangkan implementasi algoritma optimasi SGD menghasilkan akurasi 66,22%, presisi 0,69, recall 0,64, dan f1 score 0,67. Hasil ini memberikan informasi penting bahwa algoritma optimasi RMSprop menghasilkan kinerja yang jauh lebih baik daripada SGD pada klasifikasi Pneumonia Covid-19.

scholarly journals Time Scales of Submesoscale Flow Inferred from a Mooring Array

2020 ◽  
Vol 50 (4) ◽  
pp. 1065-1086
Author(s):  
Jörn Callies ◽  
Roy Barkan ◽  
Alberto Naveira Garabato
Keyword(s):  
Spatial Scale ◽  
Root Mean Square ◽  
Time Scales ◽  
Spatial Scales ◽  
Helmholtz Decomposition ◽  
Rossby Number ◽  
Mean Square ◽  
Leading Order ◽  
Rotational Component ◽  
Insight Into

AbstractWhile the distribution of kinetic energy across spatial scales in the submesoscale range (1–100 km) has been estimated from observations, the associated time scales are largely unconstrained. These time scales can provide important insight into the dynamics of submesoscale turbulence because they help quantify to what degree the flow is subinertial and thus constrained by Earth’s rotation. Here a mooring array is used to estimate these time scales in the northeast Atlantic. Frequency-resolved structure functions indicate that energetic wintertime submesoscale turbulence at spatial scales around 10 km evolves on time scales of about 1 day. While these time scales are comparable to the inertial period, the observed flow also displays characteristics of subinertial flow that is geostrophically balanced to leading order. An approximate Helmholtz decomposition shows the order 10-km flow to be dominated by its rotational component, and the root-mean-square Rossby number at these scales is estimated to be 0.3. This rotational dominance and Rossby numbers below one persist down to 2.6 km, the smallest spatial scale accessible by the mooring array, despite substantially superinertial Eulerian evolution. This indicates that the Lagrangian evolution of submesoscale turbulence is slower than the Eulerian time scale estimated from the moorings. The observations therefore suggest that, on average, submesoscale turbulence largely follows subinertial dynamics in the 1–100-km range, even if Doppler shifting produces superinertial Eulerian evolution. Ageostrophic motions become increasingly important for the evolution of submesoscale turbulence as the scale is reduced—the root-mean-square Rossby number reaches 0.5 at a spatial scale of 2.6 km.

scholarly journals An Ultrasound Study of Coarticulatory Resistance and Coarticulatory Aggression

2016 ◽  
Vol 4 (2) ◽  
pp. 314
Author(s):  
M Irfana ◽  
N Sreedevi
Keyword(s):  
Root Mean Square ◽  
Ultrasound Imaging ◽  
Imaging Technique ◽  
Mean Square ◽  
Front Vowel ◽  
Contextual Variation ◽  
Ultrasound Study ◽  
Better Than

<em><span lang="EN-IN">The term 'coarticulatory resistance' refers to the degree to which a given segment, a consonant or a vowel, resists potential interference of neighbouring segments. The phoneme has coarticulatory resistance exert stronger influence on neighbouring phoneme and exhibit less contextual variation, this characteristic termed as 'coarticulatory aggression'. The present study aims to analyse the coarticulatory resistance and coarticulatory aggression based on ultrasound imaging technique. Thirty adult Malayalam speakers participated as subjects. The stimuli consisted of VCV sequences, with C corresponding to voiced/voiceless counterparts of dental stops (/t̪/, /d̪/) or retroflex stops (/ʈ/, /ɖ/) or velar stops (/k/, /g/), in the context of vowels /a, i, u/. Measurements of coarticulation resistance of consonants, preceding vowels and following vowels were carried out based on Root Mean Square (RMS) distance between the tongue contours of vowels and consonants. Results showed that coarticulatory resistance of consonants were decreased in the order from retroflex followed by velars and dentals. High front vowel /i/ resisted coarticulation of preceding consonant better than other vowels considered. It highlights the trend of </span><span lang="EN-IN">Degree of Articulatory Constraint (DAC) </span><span lang="EN-IN">model for both consonant and vowel system.</span></em>

scholarly journals Road classification based on SVM and vehicle body vibration

2021 ◽  
Vol 268 ◽  
pp. 01074
Author(s):  
Xiaoyi Fu ◽  
Yuzhuang Zhao
Keyword(s):  
Root Mean Square ◽  
Recognition Rate ◽  
Mean Value ◽  
Driving Safety ◽  
Vehicle Body ◽  
Mean Square ◽  
Surface Information ◽  
The Mean ◽  
Reduce Energy Consumption ◽  
Better Than

Obtaining road surface information to make the vehicle run in the best condition can not only reduce energy consumption and vehicle loss, but also improve driving safety. In this paper, specific car body information was preprocessed as root mean square value, and SVM offline training was used. The recognition rate of off-road and highway can reach 98%. Compared with traditional threshold recognition, SVM has better adaptability and robustness. On the premise of keeping easy to obtain, the discrimination accuracy of the root mean square value is obviously better than the original value and the mean value.

Comparison of Flying Capacitor Fifteen Level Inverter and Thirty Three Level Inverter PWM Control Strategies

2017 ◽  
Vol 6 (2) ◽  
pp. 368
Author(s):  
Farouk Hadj Benali ◽  
Fouad Azzouz
Keyword(s):  
Root Mean Square ◽  
Output Voltage ◽  
Control Strategies ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Mean Square ◽  
Pwm Control ◽  
Simulation Results ◽  
Multicarrier Pwm ◽  
Better Than

<p>In the paper two PWM control strategies of multilevel flying capacitor inverter is proposed. The study starts with a presentation of the Flying capacitor inverter and the two PWM control strategies (SPWM and Suboptimal PWM). Than a section which presents a brief recall of the triangular multicarrier PWM and the sinusoidal multicarrier PWM. A comparison between the two PWM control strategies based on the simulation results is made. The subjects of comparison are the root mean square rms of the output voltage and the total harmonic distortion THD. The obtained results have proved that the Suboptimal PWM is better than the SPWM. Simulations are carried out by PSIM program.</p>

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

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