scholarly journals An Eye on the Future of COVID’19: Prediction of Likely Positive Cases and Fatality in India over A 30 Days Horizon using Prophet Model

2020 ◽  
pp. 1-20
Author(s):  
Vatsal Tulshyan ◽  
Dolly Sharma ◽  
Mamta Mittal
Keyword(s):  
Experimental Study ◽  
Time Series ◽  
Data Analysis ◽  
Programming Language ◽  
Missing Values ◽  
Mainland China ◽  
Real Data ◽  
Analysis Model ◽  
Data Prediction ◽  
The World

ABSTRACT Background: The coronavirus disease pandemic was initiated in Wuhan province of mainland China in December 2019 and has spread over the world. Objective: This study analyses the effects of COVID 19 based on Likely Positive Cases and fatality in India during and after the lockdown period from 24 March 2020 to 24 May 2020. Methods: Python has been used as the main programming language for data analysis and forecasting using the Prophet Model, a time series analysis model. The dataset has been preprocessed by grouping together the days for total numbers of cases and deaths on few selected dates and removed missing values present in some states. Results: The Prophet model performs better in terms of precision on the real data. Prediction depicts that during the lockdown, the total cases were rising but in a controlled manner with an accuracy of 87%. After the relaxation of lockdown rules, the predictions have shown an obstreperous situation with an accuracy of 60%. Conclusion: The resilience could have been better if the lockdown with strict norms was continued without much relaxation. The situation after lockdown has been found to be uncertain as observed by the experimental study conducted in this work.

scholarly journals Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽  
2018 ◽  
Vol 6 (7) ◽  
pp. 124 ◽  
Author(s):  
Elena Barton ◽  
Basad Al-Sarray ◽  
Stéphane Chrétien ◽  
Kavya Jagan
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Data Analysis ◽  
Real Data ◽  
Real Data Analysis ◽  
Convex Optimisation ◽  
Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

scholarly journals Analysis of Dynamical Behavior for Epidemic Disease COVID-19 with Application

2021 ◽  
Vol 12 (4) ◽  
pp. 568-577
Author(s):  
Dr. Maysoon M. Aziz, Et. al.
Keyword(s):  
Time Series ◽  
Linear System ◽  
Power Spectrum ◽  
Dynamical Behavior ◽  
Simulated Data ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Disease ◽  
The World ◽  
Non Linear System

In this paper, we will use the differential equations of the SIR model as a non-linear system, by using the Runge-Kutta numerical method to calculate simulated values for known epidemiological diseases related to the time series including the epidemic disease COVID-19, to obtain hypothetical results and compare them with the dailyreal statisticals of the disease for counties of the world and to know the behavior of this disease through mathematical applications, in terms of stability as well as chaos in many applied methods. The simulated data was obtained by using Matlab programms, and compared between real data and simulated datd were well compatible and with a degree of closeness. we took the data for Italy as an application.  The results shows that this disease is unstable, dissipative and chaotic, and the Kcorr of it equal (0.9621), ,also the power spectrum system was used as an indicator to clarify the chaos of the disease, these proves that it is a spread,outbreaks,chaotic and epidemic disease .

scholarly journals Comparison of Algorithms for Clustering Incomplete Data

2014 ◽  
Vol 39 (2) ◽  
pp. 107-127 ◽  
Author(s):  
Artur Matyja ◽  
Krzysztof Siminski
Keyword(s):  
Data Analysis ◽  
Incomplete Data ◽  
Missing Values ◽  
Real Data ◽  
Complete Data ◽  
The Other ◽  
Data Sets ◽  
Missing Value ◽  
Comparison Of Algorithms ◽  
New Algorithms

Abstract The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

scholarly journals Sparse STATIS-Dual via Elastic Net

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2094
Author(s):  
Carmen C. Rodríguez-Martínez ◽  
Mitzi Cubilla-Montilla ◽  
Purificación Vicente-Galindo ◽  
Purificación Galindo-Villardón
Keyword(s):  
Data Analysis ◽  
Programming Language ◽  
Multivariate Data Analysis ◽  
Real Data ◽  
Elastic Net ◽  
R Programming Language ◽  
Data Tables ◽  
R Programming ◽  
Penalty Technique ◽  
Data Analysis Methods

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation (Preprint)

2020 ◽  
Author(s):  
Alfonso J. Rodriguez-Morales ◽  
Ram Kumar Singh ◽  
S.S. Singh ◽  
A. K. Pandey ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Arima Model ◽  
Time Estimation ◽  
The United States ◽  
World Health ◽  
Disease Spread ◽  
Analysis Model ◽  
Indian States ◽  
The World ◽  
Confirmatory Data Analysis

BACKGROUND The highly contagious Coronavirus disease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). It is identified as the most severe pandemic disease in human history during the past 100 years. Due to non-availability of specific medication, the World Health Organization (WHO) suggested various measures of precautions and social distance in between the people for the restricting the spread of the COVID-19 disease. Various nation’s administration including the India government called for the regional and local lockdown. OBJECTIVE We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. METHODS The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. RESULTS The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. CONCLUSIONS The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. CLINICALTRIAL wqew ere re

scholarly journals PRACTICAL APPROACH FOR HYPERSPECTRAL IMAGE PROCESSING IN PYTHON

2018 ◽  
Vol XLII-3 ◽  
pp. 45-52
Author(s):  
L. Annala ◽  
M. A. Eskelinen ◽  
J. Hämäläinen ◽  
A. Riihinen ◽  
I. Pölönen
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Data Processing ◽  
Programming Language ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Analysis Pipeline ◽  
Hyperspectral Image Processing ◽  
Processing Pipeline ◽  
The World

Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.

scholarly journals ANALISIS PERMINTAAN TENAGA KERJA PADA PERUSAHAAN PERKEBUNAN SAWIT (Studi Kasus PT.SOCFINDO Perkebunan Seunagan Kabupaten Nagan Raya)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Leli Putri Ansari
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Significant Influence ◽  
Oil Palm ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Simultaneous Testing

AbstractThis study aims to analyze the effect of wages and production on oil palm plantation companies, a case study of PT.Socfindo Seunagan  Nagan Raya district. This research methode uses multiple liniear regression data analysis model. This research is quantitative and time series data for the period of 2005-2016 and data in the form of secondary data obtained from PT.Socfindo Seunagan  Nagan Raya district and Central Bureau of statistics (BPS) Nagan Raya district.Based on the results of research partial testing that wages have a significant influence on labor for demand. Where as production has no significant effect on the demand  for labor. Simultaneous testing that wages and production effect labor for demand Keyword: Wage, production, and labor for demand

scholarly journals Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation

2020 ◽  
Author(s):  
Alfonso J. Rodriguez-Morales ◽  
Ram Kumar Singh ◽  
S. S. Singh ◽  
A. K. Pandey ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Arima Model ◽  
Time Estimation ◽  
Complete Recovery ◽  
The United States ◽  
Disease Spread ◽  
Analysis Model ◽  
Indian States ◽  
The World ◽  
Confirmatory Data Analysis

Abstract Background: The highly contagious Co rona vi rus d isease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). Various nation’s administration including the India government called for the regional and local lockdown. We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. Method: The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. Results: The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. Conclusion: The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. Keywords: SARS-CoV-2; Lockdown; GDP; Nobel-Corona; Confirmatory data model

Time-Rescaling Regression Method for Exponential Decay Time Series Predictions

2021 ◽  
Author(s):  
Masaru Shintani ◽  
Ken Umeno
Keyword(s):  
Time Series ◽  
Exponential Decay ◽  
Decay Time ◽  
Real Data ◽  
Regression Method ◽  
Random Numbers ◽  
Time Axis ◽  
Exponential Functions ◽  
Exponential Law ◽  
The World

Abstract The exponential law has been discovered in various systems around the world. In this study, we introduce two existing and one proposed analytical method for exponential decay time-series predictions. The proposed method is given by a linear regression that is based on rescaling the time axis in terms of exponential decay laws. We confirm that the proposed method has a higher prediction accuracy than existing methods by performance evaluation using random numbers and verification using actual data. The proposed method can be used for analyzing real data modeled with exponential functions, which are ubiquitous in the world.

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