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Efficient estimation of influenza epidemics based on seasonal ARIMA models
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이상열 | - |
dc.contributor.author | 정지훈 | - |
dc.date.accessioned | 2017-07-19T08:46:09Z | - |
dc.date.available | 2017-07-19T08:46:09Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.other | 000000132506 | - |
dc.identifier.uri | https://hdl.handle.net/10371/131306 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 2. 이상열. | - |
dc.description.abstract | The issue of big data has received a lot of attention from researchers
working in various fields as big data sets have been considered to have great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data, a number of studies have been published to propose models that track influenza epidemics by using internet-based information. The most recent version of influenza tracking model was ARGO, which harnesses search queries from Google and reports from the Centers for Disease Control (CDC). Although the ARGO appears to predict the outbreaks of influenza accurately and outperform other existing methods such as Google Flu Trends (GFT), in this study, a classical seasonal autoregressive and integrated moving average (SARIMA) model is demonstrated to show that it outperforms the ARGO. Compared to the ARGO, the time series model incorporates more accurate seasonality of the past influenza activities, and takes less input variables into account since it utilizes only the reports provided by the CDC. The findings in this study show that the SARIMA model is a more efficient tool for estimating influenza epidemics. | - |
dc.description.tableofcontents | Chapter 1Introduction 1
Chapter 2 Data description and seasonal time series model 4 Chapter 3 Results 12 Chapter 4 Discussion 16 Bibliography 18 국문초록 20 | - |
dc.format | application/pdf | - |
dc.format.extent | 2997982 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | SARIMA model | - |
dc.subject | ARGO | - |
dc.subject | Seasonal effect | - |
dc.subject | Influenza epidemics | - |
dc.subject | Disease detection | - |
dc.subject | Influenza-like illnesses activity estimation | - |
dc.subject | Big data | - |
dc.subject.ddc | 519 | - |
dc.title | Efficient estimation of influenza epidemics based on seasonal ARIMA models | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | 19 | - |
dc.contributor.affiliation | 자연과학대학 통계학과 | - |
dc.date.awarded | 2016-02 | - |
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