scholarly journals Smallpox and Season: Reanalysis of Historical Data

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Hiroshi Nishiura ◽  
Tomoko Kashiwagi
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Reproduction Number ◽  
Negative Association ◽  
Short Term ◽  
Calendar Time ◽  
Annual Cycles ◽  
Annual Peak ◽  
Time Series Analyses ◽  
Reported Data

Seasonal variation in smallpox transmission is one of the most pressing ecological questions and is relevant to bioterrorism preparedness. The present study reanalyzed 7 historical datasets which recorded monthly cases or deaths. In addition to time series analyses of reported data, an estimation and spectral analysis of the effective reproduction number at calendar time , , were made. Meteorological variables were extracted from a report in India from 1890–1921 and compared with smallpox mortality as well as . Annual cycles of smallpox transmission were clearly shown not only in monthly reports but also in the estimates of . Even short-term epidemic data clearly exhibited an annual peak every January. Both mortality and revealed significant negative association () and correlation (), respectively, with humidity. These findings suggest that smallpox transmission greatly varies with season and is most likely enhanced by dry weather.

Measuring Value Change in Western Industrialized Societies: The Impact of Unemployment

1991 ◽  
Vol 85 (3) ◽  
pp. 905-920 ◽  
Author(s):  
Harold D. Clarke ◽  
Nitish Dutt
Keyword(s):  
Time Series ◽  
Economic Conditions ◽  
Aggregate Level ◽  
Value Change ◽  
Short Term ◽  
Cross Sectional ◽  
The Past ◽  
Value Priorities ◽  
Time Series Analyses ◽  
The Impact

During the past two decades a four-item battery administered in biannual Euro-Barometer surveys has been used to measure changing value priorities in Western European countries. We provide evidence that the measure is seriously flawed. Pooled cross-sectional time series analyses for the 1976–86 period reveal that the Euro-Barometer postmaterialist-materialist value index and two of its components are very sensitive to short-term changes in economic conditions, and that the failure to include a statement about unemployment in the four-item values battery accounts for much of the apparent growth of postmaterialist values in several countries after 1980. The aggregate-level findings are buttressed by analyses of panel data from three countries.

scholarly journals A New Multipollutant, No-Threshold Air Quality Health Index Based on Short-Term Associations Observed in Daily Time-Series Analyses

2008 ◽  
Vol 58 (3) ◽  
pp. 435-450 ◽  
Author(s):  
David M. Stieb ◽  
Richard T. Burnett ◽  
Marc Smith-Doiron ◽  
Orly Brion ◽  
Hwashin Hyun Shin ◽  
...  
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Health Index ◽  
Short Term ◽  
Daily Time Series ◽  
Quality Health ◽  
Time Series Analyses ◽  
Air Quality Health Index ◽  
Daily Time

scholarly journals Aplikasi Metode Grey Forecasting Pada Peramalan Kebutuhan Bahan Bakar Alternatif Ramah Lingkungan di PT. Indocement Tunggal Prakarsa Tbk

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Nanda Lokita Nariswari ◽  
Cucuk Nur Rosyidi
Keyword(s):  
Time Series ◽  
Alternative Energy ◽  
Historical Data ◽  
Raw Materials ◽  
Time Series Forecasting ◽  
Short Term ◽  
Grey Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
A Company

<span><em>Forecasting is one of the methods required by a company to plan the demand of raw materials in the </em><span><em>future, in order to avoid the emergence of various problems such as stock out. However, not all </em><span><em>forecasting methods can be used to forecast demand in the short term a specially a condition where the </em><span><em>company only has a few historical data. Grey method is a forecasting method which can be used to </em><span><em>predict the short-term demand. The purpose of this study is to determine how well the Grey method used </em><span><em>to predict the demand of alternative energy and compared with other forecasting methods. Mean Squared </em><span><em>Error (MSE) is used as a measure of the goodness of the method. The result of the study indicates that the </em><span><em>Grey Forecasting Methods MSE value that is smaller than other time series forecasting methods.</em></span></span></span></span></span></span></span><br /></span>

scholarly journals An improved long short-term memory neural network for stock forecast

2018 ◽  
Vol 232 ◽  
pp. 01024
Author(s):  
Liujia Lv ◽  
Weijian Kong ◽  
Jie Qi ◽  
Jue Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Error ◽  
Short Term Memory ◽  
Historical Data ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory ◽  
Closing Price ◽  
Long Short Term Memory

This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.

scholarly journals Do Changes in Threat Salience Predict Moral Content of Sermons? The Case of Friday Khutbas in Turkey

2019 ◽  
Author(s):  
Sinan Alper ◽  
Fatih Bayrak ◽  
Elif Öykü Us ◽  
Onurcan Yilmaz
Keyword(s):  
Time Series ◽  
Relative Frequency ◽  
Historical Data ◽  
Risk Index ◽  
The Other ◽  
Moral Foundations ◽  
Moral Foundations Theory ◽  
Moral Content ◽  
Societal Level ◽  
Time Series Analyses

We analyzed the content of “Friday Khutbas” delivered in Turkish mosques between January 2001 and December 2018 to test the prediction of moral foundations theory (MFT) literature that threat salience would lead to an increased endorsement of binding moral foundations. As societal-level indicators of threat, we examined (1) historical data on the number of terrorism-related news published in a Turkish newspaper, (2) geopolitical risk score of Turkey as measured by Geopolitical Risk Index, and (3) Google Trends data on the search frequency of words “terror”, “terrorism”, or “terrorist”. To measure the endorsement of moral foundations, we built a Turkish Moral Foundations Dictionary and counted the relative frequency of morality-related words in the khutbas delivered in Istanbul, Turkey. Time series analyses showed that risk salience in a certain month was positively related to endorsement of the loyalty/betrayal foundation in that month’s Friday Khutbas. There were mixed results for the other moral foundations.

scholarly journals Forecasting the spread of COVID19 in Hungary

2020 ◽  
Author(s):  
Owais Mujtaba Khanday ◽  
Samad Dadvandipour ◽  
Mohd. Aaqib Lone
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Reproduction Number ◽  
Sir Model ◽  
Exponential Smoothing ◽  
Short Term ◽  
Smoothing Methods ◽  
Series Analysis ◽  
Short Term Forecasting

AbstractTime series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.

scholarly journals INFANT POLIO IMMUNIZATION

2015 ◽  
Vol 22 (04) ◽  
pp. 507-513
Author(s):  
Muhammad Imran ◽  
Jamal Abdul Nasir ◽  
Syed Arif Ahmed Zaidi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Immunization Coverage ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Short Term ◽  
Forecasting Models ◽  
Polio Immunization ◽  
Reported Data

Poliomyelitis is a highly infectious disease but preventable by effective vaccines.Children under five year of age affected by this disease as a result a permanent paralysis.Objectives: To uncover the trend of infant polio immunization coverage through modeling isa significant concern to formulate an adequate vaccination strategies and program after theoutbreak of new cases of polio in a recent year in Pakistan. Design: The reported data ofmonthly infant polio immunization coverage to National Institute of Health, Islamabad, Pakistanfrom January 2008 to July 2013 for the present study has been taken from Pakistan bureau ofstatistics with total time series entities 67. National Institute of Health, Islamabad took the recordof per month number of doses administered ( 0-11 months )children by the registered healthcentre in pakistan. Period: January 2008 - July 2013. Setting: Pakistan bureau of statistics(Statistics House) Methods: A set of various short term time series forecasting models namelyBox-Jenkins, single moving average, double moving average, single parameter exponentialsmoothing, brown, Holts and winter models were carried out to expose the infant polioimmunization coverage trend. Results: Among the several forecasting models ARIMA modelsare chosen due to lower measure of forecast errors namely root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE). ARIMA (2,1,1), ARIMA(1,0,2), ARIMA (0,1,2) and ARIMA (2,1,1) models are established as an adequate models for theprediction of OPV-0, OPV-1, OPV-2 and OPV-3 respectively. Conclusions: With the exceptionof OPV-1 the infant polio immunization coverage is expected to rise in Pakistan.

scholarly journals Application of time series models for heating degree day forecasting

2020 ◽  
Vol 12 (1) ◽  
pp. 2137-2146
Author(s):  
Merve Kuru ◽  
Gulben Calis
Keyword(s):  
Time Series ◽  
Information Criteria ◽  
Percentage Error ◽  
Short Term ◽  
Absolute Deviation ◽  
Degree Day ◽  
Forecast Models ◽  
Time Series Analyses ◽  
Short Term Forecasting ◽  
Sarima Models

AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD.

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