Production Forecasting in Unconventional Resources using Data Mining and Time Series Analysis

2014 ◽  
Author(s):  
Siddhartha Gupta ◽  
Franz Fuehrer ◽  
Benin Chelinsky Jeyachandra
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Analysis ◽  
Unconventional Resources ◽  
Series Analysis ◽  
Production Forecasting ◽  
Using Data

Recent Mortality Trends in the Spanish Population

2002 ◽  
Vol 8 (4) ◽  
pp. 757-786 ◽  
Author(s):  
A. Felipe ◽  
M. Guillen ◽  
A. M. Perez-Marin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spanish Population ◽  
Mortality Trends ◽  
Calendar Time ◽  
Series Analysis ◽  
Univariate Time Series ◽  
Using Data ◽  
The Way

ABSTRACTOur research deals with the way that calendar time affects mortality patterns in the Spanish population, and how this information can be used to elaborate predictions. A description of the observed mortality evolution has been worked out using data from 1975 to 1993. We have used Heligman-Pollard Law number two to model the evolution of Spanish mortality over the period and using univariate time series analysis, we have obtained a prognosis for years 1994 to 2010.

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 64-80
Author(s):  
Shiya Wang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Financial Time Series ◽  
Special Kind ◽  
Future Trend ◽  
Financial Time ◽  
Series Analysis ◽  
Depth Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

scholarly journals Oscillatory fluctuations in the incidence of infectious disease and the impact of vaccination: time series analysis

1984 ◽  
Vol 93 (3) ◽  
pp. 587-608 ◽  
Author(s):  
R. M. Anderson ◽  
B. T. Grenfell ◽  
R. M. May
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Whooping Cough ◽  
Disease Incidence ◽  
Childhood Diseases ◽  
England And Wales ◽  
Incidence Data ◽  
Series Analysis ◽  
Using Data ◽  
The Impact

SummaryThis paper uses the techniques of time series analysis (autocorrelation and spectral analysis) to examine oscillatory secular trends in the incidence of infectious diseases and the impact of mass vaccination programmes on these well-documented phenomena. We focus on three common childhood diseases: pertussis and mumps (using published disease-incidence data for England and Wales) and measles (using data from England and Wales, Scotland, North America and France). Our analysis indicates highly statistically significant seasonal and longer-term cycles in disease incidence in the prevaccination era. In general, the longer-term fluctuations (a 2-year period for measles, 3-year periods for pertussis and mumps) account for most of the cyclical variability in these data, particularly in the highly regular measles series for England and Wales. After vaccination, the periods of the longer-term oscillations tend to increase, an observation which corroborates theoretical predictions. Mass immunization against measles (which reduces epidemic fluctuations) magnifies the relative importance of the seasonal cycles. By contrast, we show that high levels of vaccination against whooping cough in England and Wales appear to have suppressed the annual cycle.

Using data transformations for low-latency time series analysis

2015 ◽  
Author(s):  
Henggang Cui ◽  
Kimberly Keeton ◽  
Indrajit Roy ◽  
Krishnamurthy Viswanathan ◽  
Gregory R. Ganger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Low Latency ◽  
Latency Time ◽  
Data Transformations ◽  
Series Analysis ◽  
Using Data

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

2018 ◽  
pp. 625-658
Author(s):  
Shivani K. Purohit ◽  
Ashish K. Sharma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Comparative Analysis ◽  
Time Series Analysis ◽  
Product Development ◽  
Quality Function Deployment ◽  
Software Tool ◽  
Quality Function ◽  
Data Mining Techniques ◽  
Using Data

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

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