scholarly journals Short-term forecasting of demographic trends based on Google Trends data

2020 ◽  
Vol 15 (90) ◽  
pp. 91-118
Author(s):  
Irina E. Kalabikhina ◽  
◽  
Vladimir N. Arkhangelsky ◽  
Uliana G. Nikolaeva ◽  
Anton V. Kolotusha ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Demographic Data ◽  
Arima Model ◽  
Google Trends ◽  
Successful Implementation ◽  
Short Term ◽  
Demographic Development ◽  
Demographic Trends ◽  
Operational Monitoring ◽  
Google Search

Demographic indicators are important functions of state programs for the development of Russia, operational monitoring of demographic development is the key to the successful implementation of programs. Very often, government statistics data are published with a delay, which does not allow their use for operational monitoring and planning. In this work, the approach allows for the rapid assessment of demographic processes in the field of formation and forecasting of demographic trends in the short term based on data from query statistics from Google Trends. The relationships between the search queries and demographics are analyzed using Pearson's correlation. The analysis uses annual (total fertility rate, abortions per 100 births, abortions per 1000 women, marriages and divorces per 1000 population) and monthly data (number of births, number of marriages and divorces) by birth, marriages and abortions with and without lags. The analysis is carried out on data for Russia as a whole and for the eight most populated regions: Moscow, Moscow Region, Krasnodar Territory, St. Petersburg, Rostov Region, Sverdlovsk Region, Republic of Tatarstan, Republic of Bashkortostan. Using the temporal metrics available in Google Trends since 2004, some demographics can be predicted based on data from related queries to the Google search algorithm using the ARIMA model. Thus, it is possible to use query data as a supplement to demographic data, when building multiple regression models for demographic calculations, or use it as a proxy variable.

Musikalisasi Syi’ir Aqoid Kiai Zaini Mun’im dan Budidaya Tanaman Obat Keluarga (Toga) di Pesantren Nurul Jadid Probolinggo

2020 ◽  
Vol 1 (1) ◽  
pp. 47-58
Author(s):  
Khodijatul Qodriyah
Keyword(s):  
Herbal Medicine ◽  
Herbal Medicines ◽  
Successful Implementation ◽  
Short Term ◽  
Middle Term

The lack of students’ knowledge of their teachers’ works and the less of their consciousness to the environment are crucial problems in some islamic boardingschool, especially in Nurul Jadid. These issues will be settled by implementation of religious preaching (dakwah) with poem (syi’ir) in Syu’abul Iman of Kiai Zaini Mun’im and prefentive action to the illness through herbal medicines of family crops medicine (tanaman obat keluarga). The program is undertaken with some phases, including planting family crops medicine, making herbal medicines, musicalisation of poem in book of Syu’abul Iman, socialization of the herbal medicine and musical poem of Syu’abul Iman. These phases have been structured with long-term, middle-term, and short-term programs which were finished during approximately 4 months (Augustus – November 2019). The involvement of many parties, such as activists of environment in Nurul Jadid, has strongly influenced on the successful implementation of these programs.Keywords: Family Crops Medicine, Nurul Jadid Islamic Boardingschool, Book of Syu’abul Iman

Co może wyguglować zatroskany rodzic? Notatki o cyberpanice moralnej

2020 ◽  
pp. 49-62
Author(s):  
Jerzy Stachowicz
Keyword(s):  
Search Algorithm ◽  
Moral Panic ◽  
Digital Network ◽  
Technology Addiction ◽  
Google Search

Parents googling for information about what their children do in the digital network find a number of alarming reports. First of all, they relate to the harmfulness of technology addiction. Why? Is the google search algorithm an amplifier of the moral panic related to online practices of teenagers? This paper attempts to analyse internet discourse including the role played by technology. To describe the phenomena I discuss, I propose the term cyber panic.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

scholarly journals Google, Tell Me. Is He Gay? Masculinity, Heterosexuality, and Gendered Anxieties in Google Search Queries about Sexuality

2021 ◽  
pp. 073112142110019
Author(s):  
Emma Mishel ◽  
Tristan Bridges ◽  
Mónica L. Caudillo
Keyword(s):  
Social Desirability ◽  
Google Trends ◽  
Social Desirability Bias ◽  
Same Sex ◽  
Search Queries ◽  
Sexual Identities ◽  
Google Search

It is difficult to gauge people’s acceptance about same-sex sexualities, as responses to questionnaires are prone to social desirability bias. We offer a new proxy for understanding popular concern surrounding same-sex sexualities: prevalence of Google searches demonstrating concern over gay/lesbian sexual identities. Using Google Trends data, we find that Google searches about whether a specific person is gay or lesbian show patterned bias toward masculine searches, in that such searches are much more frequently conducted about boys and men compared with girls and women. We put these findings into context by comparing search frequencies with other popular Google searches about sexuality and otherwise. We put forth that the patterned bias toward masculine searches illustrates support for the enduring relationship between masculinity and heterosexuality and that it does so on a larger scale than previous research has been able to establish.

Abstract 115: One-Year Outcomes in Individual Domains of the Cerebral Performance Category Extended

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Katharyn L Flickinger ◽  
Melissa J Repine ◽  
Stephany Jaramillo ◽  
Allison C Koller ◽  
Margo Holm ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Return To Work ◽  
Short Term Memory ◽  
Demographic Data ◽  
Cerebral Performance Category ◽  
Logical Thinking ◽  
Short Term ◽  
Performance Category ◽  
Term Memory

Introduction: Cognitive and physical impairments are common in cardiac arrest survivors. Global measures including the Modified Rankin Scale (mRS), Cerebral Performance Category (CPC) and the 10-domain CPC-Extended (CPC-E) tend to improve over 1 year. The CPC-E is scored from 1-5 with higher scores signifying greater impairment. However, with the CPC-E, individual functional domains (alertness, logical thinking, attention, motor skills, short-term memory, basic and complex activities of daily living (ADL), mood, fatigue, and return to work) may recover at different rates. Hypothesis: We hypothesized that patients would have recovery in all domains of the CPC-E at 1 year after index cardiac arrest. Methods: A prospective cohort study of cardiac arrest survivors was conducted between 2/1/16 and 5/31/17. Chart review was done for baseline demographic data. Outcome measures including mRS, CPC, and CPC-E scores were assessed at discharge, 3 months, 6 months, and 1 year. We defined recovery of a CPC-E domain when >90% of patients had scores of 1-2 in that domain. Results: Of 71 subjects, 35 completed the CPC-E at discharge, 35 at 3 months, 25 at 6 months and 31 at 1 year. The most common reasons for exclusion were patient declined or were lost to follow up. The majority (N=37; 52%) were female, with a mean (SD) age of 58(17) years. Most arrests occurred out of hospital (N= 49; 69%), 27 (38%) had a shockable rhythm and the majority (N=37; 54%) were discharged home. CPC-E domains of alertness (N=35, 100%) logical thinking (N=35; 100%), and attention (N=33; 94%) recovered by hospital discharge. BADLs were recovered by 3 months (N=33; 94%). The majority of patients (N=24;77%) experienced slight-to-no disability or symptoms (mRS 0-2 / CPC 1-2) at 1 year follow up. CPC-E short term memory (67%), motor (87%), mood (87%), fatigue (13%), complex ADL (74%), and return to work (55%) did not recover fully by 1 year. Conclusions: In survivors of cardiac arrest, CPC-E domains of alertness, logical thinking, and attention recover rapidly, while domains of short term memory, motor, mood, fatigue, complex ADL and ability to return to work are chronically impaired 1 year after arrest. Interventions to improve recovery in these domains are needed.

Testing the weak form of efficient market hypothesis for socially responsible and Shariah indexes in the USA

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abbas Khan ◽  
Muhammad Yar Khan ◽  
Abdul Qayyum Khan ◽  
Majid Jamal Khan ◽  
Zia Ur Rahman
Keyword(s):  
Stock Prices ◽  
Arima Model ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Future Price ◽  
Short Term ◽  
Socially Responsible ◽  
Efficient Market ◽  
Content Type ◽  
The Us

Purpose By testing the weak form of efficient market hypothesis (EMH) this study aims to forecast the short-term stock prices of the US Dow and Jones environmental socially responsible index (SRI) and Shariah compliance index (SCI). Design/methodology/approach This study checks the validity of the weak form of EMH for both SCI and SRI prices by using different parametric and non-parametric tests, i.e. augmented Dickey-Fuller test, Philip-Perron test, runs test and variance ratio test. If the EMH is invalid, the research further forecasts short-term stock prices by applying autoregressive integrated moving average (ARIMA) model using daily price data from 2010 to 2018. Findings The research confirms that a weak form of EMH is not valid in the US SRI and SCI. The historical data can predict short-term future price movements by using technical ARIMA model. Research limitations/implications This study provides better guidance to risk-averse national and international investors to earn higher returns in the US SRI and SCI. This study can be extended to test the EMH of Islamic equity in the Middle East and North Africa region and other top Islamic indexes in the world. Originality/value This study is a new addition to the existing literature of equity investment and price forecasting by comparing and investigating the market efficiency of two interrelated US SRI and SCI.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

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