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A multi-scale method for forecasting oil price with multi-factor search engine data
یک روش چند مقیاس برای پیش بینی قیمت نفت با داده های موتور جستجوی چند عاملی-2020 With the boom in big data, a promising idea for using search engine data has emerged and improved international
oil price prediction, a hot topic in the fields of energy system modelling and analysis. Since different
search engine data drive the oil price in different ways at different timescales, a multi-scale forecasting methodology
is proposed that carefully explores the multi-scale relationship between the oil price and multi-factor
search engine data. In the proposed methodology, three major steps are involved: (1) a multi-factor data process,
to collect informative search engine data, reduce dimensionality, and test the predictive power via statistical
analyses; (2) multi-scale analysis, to extract matched common modes at similar timescales from the oil price and
multi-factor search engine data via multivariate empirical mode decomposition; (3) oil price prediction, including
individual prediction at each timescale and ensemble prediction across timescales via a typical forecasting
technique. With the Brent oil price as a sample, the empirical results show that the novel methodology
significantly outperforms its original form (without multi-factor search engine data and multi-scale analysis),
semi-improved versions (with either multi-factor search engine data or multi-scale analysis), and similar
counterparts (with other multi-scale analysis), in both the level and directional predictions. Keywords: Big data | Search engine data | Google trends | Multivariate empirical mode decomposition | Oil price forecasting |
مقاله انگلیسی |
2 |
Forecasting crude oil price with multilingual search engine data
پیش بینی قیمت نفت خام با داده های موتور جستجو چند زبانه-2020 In the big data era, search engine data (SED) has presented new opportunities for
improving crude oil price prediction; however, the existing research were confined to
single-language (mostly English) search keywords in SED collection. To address such a
language bias and grasp worldwide investor attention, this study proposes a novel
multilingual SED-driven forecasting methodology from a global perspective. The proposed
methodology includes three main steps: (1) multilingual index construction, based on
multilingual SED; (2) relationship investigation, between the multilingual index and crude oil
price; and (3) oil price prediction, with the multilingual index as an informative predictor.
With WTI spot price as studying samples, the empirical results indicate that SED have a
powerful predictive power for crude oil price; nevertheless, multilingual SED statistically
demonstrate better performance than single-language SED, in terms of enhancing prediction
accuracy and model robustness. Keywords: Big data | Multilingual search engine index | Crude oil price forecasting | Google Trends | Artificial intelligence |
مقاله انگلیسی |
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The Dark Web and cannabis use in the United States: Evidence from a big data research design
استفاده از وب تاریک و حشیش در ایالات متحده: شواهدی از طراحی تحقیقات داده های بزرگ-2020 Background: Cannabis is one of the most commonly sold drugs on cryptomarkets. Because of the anonymitygranting
functions of Tor, no study has traced the within-country effect of the Dark Web on cannabis consumption
patterns. This article uses a big data research design to examine the association between revealed
interest in the Dark Web and self-reported cannabis use within US states from 2011 when Silk Road launched to
2015 when Operation Onymous shuttered nine markets.
Methods: This study uses mixed effects ordinary least squared regressions to analyze U.S. state/year panel data,
using robust standard errors to correct for heteroscedasticity. Marginal effect plots illustrate substantive effects.
The dataset consists of state-level variables drawn from the Uniform Crime Report (UCR), the American
Community Survey (ACS), the National Survey on Drug Use and Health, the Correlates of State Policy Project,
and the Bureau of Justice Statistics Justice Expenditure and Employment Extracts. Data for the Dark Web interest
measure are drawn from Google Trends. The proxy for Dark Web interest is an index of eight Dark Web related
search queries.
Results: The regression analysis indicates that Dark Web interest in US states positively correlates with cannabis
consumption rates overall and among older adults (26+), but not youth (12–17) or younger adults (18–25).
Additionally, Dark Web interest is positively associated with more frequent cannabis usage rates (i.e. use in the
past month, excluding first time use) both overall and among older adults, but not among youth or younger
adults. Dark Web interest does not correlate with casual use (i.e. use in the last year, excluding use in the past
month) for any age bracket. Interacting Dark Web interest with state-level legalization regimes indicates that the
association between Dark Web interest and cannabis consumption in the past year is no different in medically
legalized states and amplified in states with recreational legalization. Lastly, the Dark Web interest term does not
correlate with first time cannabis either overall or for any age category.
Conclusions: Interest in the Dark Web is associated with increased cannabis use in U.S. states from 2011–2015,
but the effect is concentrated in states with more frequent cannabis users, older users, and in states with recreational
legalization of cannabis. Keywords: Dark web | Cryptomarkets | Cannabis | Silk Road | Google Trends | Cannabis Legalization |
مقاله انگلیسی |
4 |
Open data mining for Taiwan’s dengue epidemic
داده کاوی گسترش اپیدمی تب دانگ تایوان-2018 By using a quantitative approach, this study examines the applicability of data mining technique to discover
knowledge from open data related to Taiwan’s dengue epidemic. We compare results when Google trend data are
included or excluded. Data sources are government open data, climate data, and Google trend data. Research
findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest
classification power followed by climate variables (temperature and humidity), whereas gender and age show
the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (re
spectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in
the context of public health care.
Keywords: Open data ، Data mining ، Dengue epidemic ، Google trend ، Simplicity |
مقاله انگلیسی |
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Google Trends and tourists arrivals: Emerging biases and proposed corrections
گرایشات گوگل و ورودی های گردشگران: انحراف های پدید آمده و تصحیح های پیشنهادی-2018 As search engines constitute a leading tool in planning vacations, researchers have adopted search engine query data to predict the consumption of tourism products. However, when the prevailing shares of visitors come from countries in different languages and with different dominating search engine platforms, the identification of the aggregate search intensity index to forecast overall international arrivals, becomes challenging since two critical sources of bias are involved. After defining the language bias and the platform bias, this study focuses on a destination with a multilingual set of source markets along with different dominating search engine platforms. We analyze monthly data (2004–2015) for Cyprus with two non-causality testing procedures. We find that the corrected aggregate search engine volume index, adjusted for different search languages and different search platforms, is preferable in forecasting international visitor volumes compared to the non-adjusted index.
keywords: Web search intensity |Google Trends |Tourists arrivals |
مقاله انگلیسی |
6 |
Online big data-driven oil consumption forecasting with Google trends
پیش بینی مصرف نفت با استفاده از داده های بزرگ آنلاین با روند گوگل-2018 The rapid development of big data technologies and the Internet provides a rich mine
of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption
— an essential but uncertain factor in the oil supply chain. An online big data-driven oil
consumption forecasting model is proposed that uses Google trends, which finely reflect
various related factors based on a myriad of search results. This model involves two main
steps, relationship investigation and prediction improvement. First, cointegration tests and
a Granger causality analysis are conducted in order to statistically test the predictive power
of Google trends, in terms of having a significant relationship with oil consumption. Second,
the effective Google trends are introduced into popular forecasting methods for predicting
both oil consumption trends and values. The experimental study of global oil consumption
prediction confirms that the proposed online big-data-driven forecasting work with Google
trends improves on the traditional techniques without Google trends significantly, for both
directional and level predictions.
Keywords: Google trends ، Oil consumption forecasting ، Online big data ، Supply chain ، Artificial intelligence |
مقاله انگلیسی |
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Ten years of research change using Google Trends: From the perspective of big data utilizations and applications
ده سال از تغییرات تحقیق با استفاده از Google Trends: از دیدگاه استفاده از داده های بزرگ و برنامه های کاربردی-2018 This study seeks to analyze the trends in research studies in the past decade which have utilized Google Trends, a
new source of big data, to examine how the scope of research has expanded. Our purpose is to conduct a
comprehensive and objective research into how the public use of Big Data from web searches has affected
research, and furthermore, to discuss the implications of Google Trends in terms of Big Data utilization and
application. To this end, we conducted a network analysis on 657 research papers that used Google Trends. We
also identified the important nodes of the networks and reviewed the research directions of representative
papers. The study reveals that Google Trends is used to analyze various variables in a wide range of areas,
including IT, communications, medicine, health, business and economics. In addition, this study shows that
research using Google Trends has increased dramatically in the last decade, and in the process, the focus of
research has shifted to forecasting changes, whereas in the past the focus had been on merely describing and
diagnosing research trends, such as surveillance and monitoring. This study also demonstrates that in recent
years, there has been an expansion in analysis in linkage with other social Big Data sources, as researchers
attempt to overcome the limitations of using only search information. Our study will provide various insights for
researchers who utilize Google Trends as well as researchers who rely on various other sources of Big Data in
their efforts to compare research trends and identify new areas for research.
Keyword: Google Trends ، Big data utilization ، Big data application ، Networks analysis ، Author keyword ، Science Journal Classification ، Clustering |
مقاله انگلیسی |
8 |
آلودگی مشاهده شده و ورود گردشگران در چین
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 14 آلودگی ذاتا" با اندیشه های منفی همراه است به ویژه وقتی که آن آلودگی بتواند برای سلامتی خطرناک باشد. با این حال، اینکه آلودگی تا چه حد گردشگری را دلسرد می کند هنوز به صورت تجربی مطالعه نشده است. در این مقاله تحقیقی، ما اثر متقابل بین آلودگی مشاهده شده و ورود گردشگر به چین را ازطریق یک مدل VAR بررسی می کنیم. نگرانی ها درباره آلودگی توسط داده های گرایشی گوگل اندازه گیری شده است. ما پی بردیم که آلودگی مشاهده شده میزان ورود گردشگر را کاهش می دهد. نتایج ما نشان می دهد که افزایش ناراحتی از آلودگی می تواند ازنظر اقتصادی به بخش گردشگری آسیب برساند.
کلیدواژه ها: ورود گردشگران | آلودگی | آلودگی مشاهده شده | داده های موتور جستجوگر | گرایشات گوگل |
مقاله ترجمه شده |
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Perceived pollution and inbound tourism in China
آلودگی درک شده و گردشگری ورودی در چین-2017 Pollution inherently conjures negative thoughts; particularly when that pollution can be hazardous to your
health. However, the extent to which pollution discourages tourism has yet to be studied empirically. In this research letter, we examine the interaction between perceived pollution and inbound tourism in China through a VAR model. Concerns about pollution are measured by Google Trends data. We find that perceived pollution lowers inbound tourism. Our results show that the rising unease about pollution could hurt the tourism sector
in an economy.
Keywords: Inbound tourism | Pollution | Perceived pollution | Search engine data | Google Trends |
مقاله انگلیسی |
10 |
Mining web-based data to assess public response to environmental events
داده کاوی مبتنی بر وب برای ارزیابی پاسخ های عمومی به حوادث زیست محیطی-2015 We explore how the analysis of web-based data, such as Twitter and Google Trends, can be used to assess the social relevance of an environmental accident. The concept and methods are applied in the shutdown of drinking water supply at the city of Toledo, Ohio, USA. Toledo's notice, which persisted from August 1 to 4, 2014, is a high-profile event that directly influenced approximately half a million people and received wide recognition. The notice was given when excessive levels of microcystin, a byproduct of cyanobacteria blooms, were discovered at the drinking water treatment plant on Lake Erie. Twitter mining results illustrated an instant response to the Toledo incident, the associated collective knowledge, and public perception. The results from Google Trends, on the other hand, revealed how the Toledo event raised public attention on the associated environmental issue, harmful algal blooms, in a long-term context. Thus, when jointly applied, Twitter and Google Trend analysis results offer complementary perspectives. Web content aggregated through mining approaches provides a social standpoint, such as public perception and interest, and offers context for establishing and evaluating environmental man- agement policies.© 2014 Elsevier Ltd. All rights reserved.
Keywords: Twitter | Google trends | Social media | Web search trends | Data mining | Algal blooms | Public perception and interest |
مقاله انگلیسی |