scholarly journals Kalman Filter Based Short Term Prediction Model for COVID-19 Spread

2020 ◽  
Author(s):  
Suraj Kumar ◽  
Koushlendra Kumar Singh ◽  
Prachi Dixit ◽  
Manish Kumar Bajpai
Keyword(s):  
Kalman Filter ◽  
Random Forest ◽  
Test Data ◽  
Pearson Correlation ◽  
Medical Emergency ◽  
Contributing Factors ◽  
Short Term ◽  
Long Term Forecasting ◽  
Short Term Prediction

AbstractCOVID-19 has emerged as global medical emergency in recentdecades. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID-19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data are integrated and passed into different Machine Learning Models to check the fit. Ensemble Learning Technique,Random Forest, gives a good evaluation score on the test data. Through this technique, various important factors are recognised and their contribution to the spread is analysed. Also, linear relationship between various features is plotted through heatmap of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of COVID19, which shows good result on test data. The inferences from Random Forest feature importance and Pearson Correlation gives many similarities and some dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals ABCD3-I and ABCD2 Scores in a TIA Population with Low Stroke Risk

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fredrik Ildstad ◽  
Hanne Ellekjær ◽  
Torgeir Wethal ◽  
Stian Lydersen ◽  
Hild Fjærtoft ◽  
...  
Keyword(s):  
Stroke Risk ◽  
Risk Scores ◽  
Short Term ◽  
Clinical Trial Registration ◽  
Long Term Follow Up ◽  
Risk Patients ◽  
Abcd2 Score ◽  
Short Term Prediction

Objectives. We aimed to evaluate the ABCD3-I score and compare it with the ABCD2 score in short- (1 week) and long-term (3 months; 1 year) stroke risk prediction in our post-TIA stroke risk study, MIDNOR TIA. Materials and Methods. We performed a prospective, multicenter study in Central Norway from 2012 to 2015, enrolling 577 patients with TIA. In a subset of patients with complete data for both scores ( n = 305 ), we calculated the AUC statistics of the ABCD3-I score and compared this with the ABCD2 score. A telephone follow-up and registry data were used for assessing stroke occurrence. Results. Within 1 week, 3 months, and 1 year, 1.0% ( n = 3 ), 3.3% ( n = 10 ), and 5.2% ( n = 16 ) experienced a stroke, respectively. The AUCs for the ABCD3-I score were 0.72 (95% CI, 0.54 to 0.89) at 1 week, 0.66 (95% CI, 0.53 to 0.80) at 3 months, and 0.68 (0.95% CI, 0.56 to 0.79) at 1 year. The corresponding AUCs for the ABCD2 score were 0.55 (95% CI, 0.24 to 0.86), 0.55 (95% CI, 0.42 to 0.68), and 0.63 (95% CI, 0.50 to 0.76). Conclusions. The ABCD3-I score had limited value in a short-term prediction of subsequent stroke after TIA and did not reliably discriminate between low- and high-risk patients in a long-term follow-up. The ABCD2 score did not predict subsequent stroke accurately at any time point. Since there is a generally lower stroke risk after TIA during the last years, the benefit of these clinical risk scores and their role in TIA management seems limited. Clinical Trial Registration. This trial is registered with NCT02038725 (retrospectively registered, January 16, 2014).

scholarly journals Evaluation of installation effects on set-up of field displacement piles in sand

2019 ◽  
Vol 56 (4) ◽  
pp. 461-472 ◽  
Author(s):  
Ivana Anusic ◽  
Barry M. Lehane ◽  
Gudmund R. Eiksund ◽  
Morten A. Liingaard
Keyword(s):  
Test Data ◽  
Impact Frequency ◽  
Short Term ◽  
Factors Affecting ◽  
Installation Effects ◽  
Field Displacement ◽  
Small Influence ◽  
Set Up ◽  
Shaft Friction

The paper presents results from a new series of tests on displacement piles in sand, involving different installation modes, and combines these with results from previous tests at the same site as well as with test data at two other well-investigated sand sites to provide fresh insights into factors affecting “short-term” capacity and set-up of shaft friction. It is shown that the shaft capacity measured shortly after installation reduces systematically with the logarithm of the number of impact blows or jacking increments per unit shaft area imparted during installation. However, the degree of set-up of shaft friction for piles increases with an increase in the number of blows, and piles installed using a large number of blows can attain highest “long-term” shaft capacities, despite having the lowest short-term capacity. The tests indicated that the driving impact frequency had a relatively small influence on shaft friction, while piles installed by vibration attain short-term capacities comparable to driven impact piles, but showed negative set-up.

scholarly journals On Retrospecting Human Dynamics with Attention

2019 ◽  
Author(s):  
Minjing Dong ◽  
Chang Xu
Keyword(s):  
Human Motion ◽  
Motion Prediction ◽  
Sufficient Information ◽  
Short Term ◽  
Human Dynamics ◽  
Time Horizons ◽  
Term Prediction ◽  
Error Accumulation ◽  
Short Term Prediction

Deep recurrent neural networks have achieved impressive success in forecasting human motion with a sequence to sequence architecture. However, forecasting in longer time horizons often leads to implausible human poses or converges to mean poses, because of error accumulation and difficulties in keeping track of longerterm information. To address these challenges, we propose to retrospect human dynamics with attention. A retrospection module is designed upon RNN to regularly retrospect past frames and correct mistakes in time. This significantly improves the memory of RNN and provides sufficient information for the decoder networks to generate longer term prediction. Moreover, we present a spatial attention module to explore and exploit cooperation among joints in performing a particular motion. Residual connections are also included to guarantee the performance of short term prediction. We evaluate the proposed algorithm on the largest and most challenging Human 3.6M dataset in the field. Experimental results demonstrate the necessity of investigating motion prediction in a self audit manner and the effectiveness of the proposed algorithm in both short term and long term predictions.

scholarly journals On Modelling Seasonal Arima Series: Application on Road Accidents in Northeast Algeria

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Kalman Filter ◽  
Parametric Identification ◽  
Series Data ◽  
Percentage Error ◽  
Filter Method ◽  
Paper Study ◽  
Road Accidents ◽  
Short Term ◽  
Kalman Filter Method ◽  
Short Term Prediction

This paper study and modeless a number of road accidental injuries in the region of Skikda (northeast Algeria) according to Box- Jenkins method using EViews software using series data from January 2001 to December 2016. Also, Kalman filter method is given. To this end, Kalman filter method is used for short term prediction and parametric identification purpose. The other side, a comparative study is given to compare between the two methods by de following criteria: Mean absolute percentage error (MAPE), root mean square percentage error (RMSPE) and the Theils’s U statistic. This application used Eviews 5.0 and SPSS 26 software’s.

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