• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.227-233
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• 2016

Google Trends provides weekly information on keyword search frequency on the Google search engine. Search volume patterns for the search keyword can also be analyzed based on category and by the location of those making the search. Also, Google provides “Hot searches” and “Top charts” including top and rising searches that include the search keyword. All this information is kept up to date, and allows trend comparisons by providing past weekly figures. In this study, we present a predictive model for TV markets using the searched data in Google search engine (Google Trend data). Using a predictive model for the market and analysis of the Google Trend data, we obtained an efficient and meaningful result for the TV market, and also determined highly ranked countries and cities. This method can provide very useful information for TV manufacturers and others.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of Platform Technology
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• v.11 no.4
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• pp.19-34
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• 2023

In order to identify the trends and insights of 'startups' in the global era, we conducted an in-depth trend analysis of the global startup ecosystem using Google Trends, a big data analysis platform. For the validity of the analysis, we verified the correlation between the keywords 'startup' and 'global' through BIGKinds. We also conducted a network analysis based on the data extracted using Google Trends to determine the frequency of searches for the keyword or term 'startup'. The results showed a strong positive linear relationship between the keywords, indicating a statistically significant correlation (correlation coefficient: +0.8906). When exploring global startup trends using Google Trends, we found a terribly similar linear pattern of increasing and decreasing interest in each country over time, as shown in Figure 4. In particular, startup interest was low in the range of 35 to 76 from mid-2020 due to the COVID-19 pandemic, but there was a noticeable upward trend in startup interest after March 2022. In addition, we found that the interest in startups in each country except South Korea is very similar, and the related topics are startup company, technology, investment, funding, and keyword search terms such as best startup, tech, business, invest, health, and fintech are highly correlated.

• The Journal of Information Systems
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• v.29 no.1
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• pp.289-301
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• 2020

Purpose This study analyzes whether Internet search of cryptocurrency has a causal relationship to return and volatility of cryptocurrency. Design/methodology/approach Google Trend was used as a measure of the level of Internet search, and the parametric tests of Granger causality in the 1st moment and the 2nd moment were adopted as the analysis method. We used Bitcoin's dollar-based price, which is the No. 1 market value among cryptocurrency. Findings The results showed that the Internet search measured by Google Trends has a causal relationship to cryptocurrency in both average and volatility, while there is a difference in causality and its degree according to the search area and category that Google Trend user should set. Because the Granger causality is based on the improvement of prediction, the analysis results of this study indicate that Internet search can be used as a leading indicator in predicting return and volatility of cryptocurrency.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.95-101
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• 2013

Social media has many advantages. It can gain latest information with real time, be spread rapidly, easily be reproduced and distributed regardless of its form. These advantages can result in real time predictions using the latest information, which is possible due to the increase in social demand for more quick and accurate economic variable predictions. In this paper we adopted ARIMAX and ECM model to predict the unemployment rate and as a social information we used the Google Index provided by Google Trend. Also we used News Index as a domestic social information. The process of fitting statistical model considered in this paper can be adopted to predict various socio/economic indices as well as unemployment rate.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.300-308
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• 2018

Objectives: Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics. Methods: The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP. Results: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation. Conclusions: Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of Oriental Neuropsychiatry
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• v.34 no.3
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• pp.235-245
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• 2023

Objectives: The aim of this study was to analyze public and researcher interests in suicide and related illnesses and acupuncture and acupressure treatment using Google Trends and some electronic databases. Methods: Search results for keywords "suicide," "acupuncture," "acupressure," and several illnesses related to suicide were analyzed in Google Trends from January 2004 to June 2023. Illnesses included anxiety, depression (including major depressive disorder), schizophrenia, bipolar disorder, post- traumatic stress disorder (PTSD), eating disorder (including anorexia nervosa and bulimia nervosa), substance use disorder, autism spectrum disorder, personality disorder (including borderline person- ality disorder), and chronic pain. Search results were extracted using relative search volume (RSV) scores between 0 and 100. Search terms were also searched in online databases, including PubMed, CNKI, and OASIS, to estimate the number of related studies, and descriptive analysis was conducted. Results: Google Trends analysis showed a strong positive correlation between the RSVs of "suicide and depression," "acupuncture and chronic pain," and "acupressure and PTSD." The electronic database search results produced numerous studies published on "suicide and depression," "acupuncture and depression," and "acupressure and anxiety." High interest in "suicide and depression," "acupuncture and chronic pain," and "acupressure and anxiety" was seen among the public and researchers. Interest in "suicide and chronic pain," "acupuncture and eating disorder," and "acupressure and PTSD" was higher in the public than among researchers, while "anxiety and suicide" and "anxiety and acu- puncture" showed opposite trends. Conclusions: The results of this research enable an understanding of public and researcher interest in suicide, acupuncture, acupressure, and suicide-related illnesses. The results also provide a basis for fu- ture research and examining public health implications in Korean medicine.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.67-72
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• 2018

On the internal based search, the big data inference, which is failed in the president's election in the United States of America in 2016, is failed, because the prediction method is used on the base of the searching numerical value of a candidate for the presidency. Also the Flu Trend service is opened by the Google in 2008. But the Google was embarrassed for the fame's failure for the killing flu prediction system in 2011 and the prediction of presidential election in 2016. In this paper, using the virtual vote algorithm for virtual election and data mining method, the election prediction algorithm is proposed and unpacked. And also the WEKA DB is unpacked. Especially in this paper, using the K means algorithm and XEDOS tools, the prediction of election results is unpacked efficiently. Also using the analysis of the WEKA DB, the smart election prediction system is proposed in this paper.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.433-440
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• 2020

The purpose of this study is to analyze the research trend by conducting a systematic review of domestic research on clinical fieldwork in occupational therapy. From 2000 to 2019, papers registered in the Google Scholar, RISS, KISS, and National Library of Korea databases were searched. The search term was "Occupational Therapy" AND "Clinical Fieldwork", and a total of 8 studies were finally selected. As a result of analyzing the research trend, the most frequently used data collection method were surveys 6 studies(75.0%), and the most frequent subject were occupational therapy students was 6 studies(75.0%). The most frequent main theme was satisfaction of clinical fieldwork 4 studies(50.0%). In the future, it was confirmed that quantitative and qualitative growth of clinical research related to occupational therapy in Korea is needed.