scholarly journals Patent Keyword Analysis using Time Series and Copula Models

2019 ◽  
Vol 9 (19) ◽  
pp. 4071 ◽  
Author(s):  
Kim ◽  
Yoon ◽  
Hwang ◽  
Jun
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Patent Data ◽  
Analysis Model ◽  
Copula Models ◽  
Keyword Analysis ◽  
Logit Function ◽  
Patent Documents ◽  
Major Factors ◽  
Over Time

The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.

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 Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

2020 ◽  
Vol 10 (2) ◽  
pp. 570 ◽  
Author(s):  
Daiho Uhm ◽  
Jea-Bok Ryu ◽  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Bayesian Inference ◽  
Prior Knowledge ◽  
Interval Estimation ◽  
Patent Data ◽  
Domain Experts ◽  
Technology Analysis ◽  
Patent Documents

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

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.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

The Influence of Leadership on the Transfer of Knowledge and Work Effectivity at Secretariat Office in Boven Digoel Regency

2020 ◽  
Vol 2 (4) ◽  
pp. 44-52
Author(s):  
Yoseph Awunim ◽  
Abdul Rahman Kadir ◽  
Mahlia Muis
Keyword(s):  
Data Analysis ◽  
Knowledge Transfer ◽  
Path Analysis ◽  
Positive Impact ◽  
Direct Impact ◽  
Transfer Of Knowledge ◽  
Analysis Model ◽  
Work Effectiveness ◽  
Statistical Results ◽  
Path Analysis Model

The research objective is to analyze the direct impact of leadership toward transfer knowledge and work effectiveness in Boven Digoel. Data analysis in this research is quantitative using a path analysis model (path analysis) with the help of Smart PLS Software version 3.2 .8. The research distributed questionnaires to 89 respondents of officers assigned at the secretariat office in Boven Digoel Regency. On the basis of statistical results, it was found that leadership can be said to have impacted positively and significant knowledge transfer and work effectiveness. The knowledge transfer also has a positive impact on work effectiveness.

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

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