Forecasting energy data with a time lag into the future and Google trends

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences.

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Google Trends: Is it a real tool to predict the future of bariatric surgery or merely a marketing landmine?

Surgery for Obesity and Related Diseases ◽

10.1016/j.soard.2013.11.009 ◽

2014 ◽

Vol 10 (3) ◽

pp. 538-539 ◽

Cited By ~ 5

Author(s):

Dana A. Telem ◽

Aurora D. Pryor

Keyword(s):

Bariatric Surgery ◽

Google Trends ◽

The Future

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Forecasting the future number of Pertussis cases using data from Google Trends

Heliyon ◽

10.1016/j.heliyon.2021.e08386 ◽

2021 ◽

pp. e08386

Author(s):

Dominik Nann ◽

Mark Walker ◽

Leonie Frauenfeld ◽

Tamás Ferenci ◽

Mihály Sulyok

Keyword(s):

Google Trends ◽

The Future ◽

Using Data

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MINAT PENCARIAN MASYRAKAT TERHADAP ALAT PELINDUNG DIRI SELAMA PANDEMI COVID-19 DI INDONESIA: ANALISIS GOOGLE TRENDS

Jurnal Kesmas (Kesehatan Masyarakat) Khatulistiwa ◽

10.29406/jkmk.v8i3.2580 ◽

2021 ◽

Vol 8 (3) ◽

pp. 122

Author(s):

Mohammad Hilal Atthariq Ramadhan ◽

Umrohtul Habibah ◽

Ayu Kartika Putri ◽

Tasya Lianda Sari ◽

Fathur Afif Moulana ◽

...

Keyword(s):

Time Lag ◽

Google Trends ◽

Hand Sanitizer

Kasus COVID-19 di Indonesia yang semakin meningkat menyebabkan masyarakat harus melindungi diri dari penyebaran infeksi melalui pemakaian Alat Pelindung Diri (APD). Penelitian ini bertujuan mengamati respon masyarakat Indonesia dalam mencari data mengenai ketiga APD tersebut terhadap COVID-19 selama pandemi melalui Google trends. Pencarian kata kunci mengenai Alat Perlindungan Diri (APD) yaitu masker, hand sanitizer, dan face shield melalui Google Trends periode 11 Maret−3 September 2020. Data pencarian RSV dan perbandingan kasus harian dilakukan berdasarkan analisis korelasi Pearson dan time-lag dengan signifikansi

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Increase in Google Trends Regarding Telogen Effluvium Due To COVID-19

SKIN The Journal of Cutaneous Medicine ◽

10.25251/skin.5.2.8 ◽

2021 ◽

Vol 5 (2) ◽

pp. 137-139

Author(s):

Jasmine Garg ◽

Abigail Cline ◽

Frederick Pereira

Keyword(s):

United States ◽

Hair Loss ◽

Public Interest ◽

The United States ◽

Google Trends ◽

Therapeutic Interventions ◽

The Public ◽

Search Terms ◽

The Future ◽

Before And After

Objective: The purpose of this study was to assess the public interest in the United States of telogen effluvium before and after the COVID-19 pandemic in order to investigate the best therapeutic interventions for dermatologists in the future. Methods: We performed Google TrendsTM search for “COVID hair loss”, “telogen effluvium” and “hair loss” between 5/1/20 and 8/16/20. Conclusion: All three terms have increased in popularity for search terms since mid-March and were the most prevalent in the states that experienced the earliest increase in number of coronavirus cases.

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Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study

F1000Research ◽

10.12688/f1000research.25525.2 ◽

2021 ◽

Vol 9 ◽

pp. 1201

Author(s):

Dewi Rokhmah ◽

Khaidar Ali ◽

Serius Miliyani Dwi Putri ◽

Khoiron Khoiron

Keyword(s):

Alternative Medicine ◽

Public Interest ◽

Time Lag ◽

Google Trends ◽

Rank Test ◽

Search Term ◽

Alternative Medicines ◽

Search Volume ◽

Relative Search Volume ◽

Time Lag Correlation

Background: The COVID-19 pandemic has triggered individuals to increase their healthy behaviour in order to prevent transmission, including improving their immunity potentially through the use of alternative medicines. This study aimed to examine public interest on alternative medicine during the COVID-19 pandemic using Google Trends in Indonesia. Methods: Employing a quantitative study, the Spearman rank test was used to analyze the correlation between Google Relative Search Volume (RSV) of various search terms, within the categories of alternative medicine, herbal medicine and practical activity, with COVID-19 cases. In addition, time lag correlation was also investigated. Results: Public interest toward alternative medicine during COVID-19 pandemic in Indonesia is dramatically escalating. All search term categories (alternative medicine, medical herbal, and alternative medicine activities) were positively associated with COVID-19 cases (p<0.05). The terms ‘ginger’ (r=0.6376), ‘curcumin’ (r=0.6550) and ‘planting ginger’ (0.6713) had the strongest correlation. Furthermore, time lag correlation between COVID-19 and Google RSV was also positively significant (p<0.05). Conclusion: Public interest concerning alternative medicine related terms dramatically increased after the first COVID-19 confirmed case was reported in Indonesia. Time lag correlation showed good performance using weekly data. The Indonesian Government will play an important role to provide and monitor information related to alternative medicine in order for the population to receive the maximum benefit.

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Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study

10.1101/2021.03.09.21253186 ◽

2021 ◽

Author(s):

Alessandro Rabiolo ◽

Eugenio Alladio ◽

Esteban Morales ◽

Andrew I McNaught ◽

Francesco Bandello ◽

...

Keyword(s):

Time Series ◽

Web Application ◽

Moving Average ◽

Time Lag ◽

Search Behavior ◽

Google Trends ◽

Optimal Time ◽

Time Range ◽

Prediction Ability ◽

Time Series Modelling

ABSTRACTBackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively).InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic.FundingEOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

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Forecasting the COVID-19 Epidemic By Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study (Preprint)

10.2196/preprints.28876 ◽

2021 ◽

Author(s):

Alessandro Rabiolo ◽

Eugenio Alladio ◽

Esteban Morales ◽

Andrew Ian McNaught ◽

Francesco Bandello ◽

...

Keyword(s):

Time Series ◽

Predictive Models ◽

Web Application ◽

Moving Average ◽

Time Lag ◽

Search Behavior ◽

Principal Component ◽

Google Trends ◽

Time Range ◽

Prediction Ability

BACKGROUND Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. OBJECTIVE The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. METHODS An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. RESULTS The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). CONCLUSIONS The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

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ANALISIS PENELUSURAN MASKER SEBAGAI PROTOKOL KESEHATAN SAAT PANDEMI COVID-19 DI INDONESIA: STUDI GOOGLE TRENDS

VISIKES: Jurnal Kesehatan Masyarakat ◽

10.33633/visikes.v20i1.4191 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Ully Febra Kusuma ◽

Nurunnisa Arsyad ◽

Melissa Shalimar Lavinia ◽

Selvia Rahayu ◽

Muhammad Khairul Kahfi Pasaribu ◽

...

Keyword(s):

Public Interest ◽

Keyword Search ◽

Time Series Data ◽

Preventive Measures ◽

Time Lag ◽

Google Trends ◽

Series Data ◽

Qualitative And Quantitative ◽

Search Results ◽

Qualitative And Quantitative Study

AbstrakMedical masks are also used by people at risk who are indicated to need them. The supply of medical masks is limited, the general public is encouraged to use non-medical masks or cloth masks. This article will discuss the comparison of search results for sensi masks, cloth masks and N-95 masks using google trend analysis. This research method is a qualitative and quantitative study using time series data with quantitative analysis, time-lag correlation is used to assess whether an increase in GT data is correlated with an increase in COVID-19 cases. Data from google trends regarding keywords related to one of the preventive measures for COVID-19, namely masks such as "sensi masks", "cloth masks" and "N-95 masks". Each search interest usually reaches a peak depending on the situation and conditions that occur at that time. The keyword search for "N-95 masks" experienced a peak when 2 Indonesians were confirmed positive for COVID-19, namely on March 2, 2020 and the day after that the keyword "sensi mask" also experienced the highest peak of searches. The keyword search for "cloth masks" peaked on March 6, 2020, when the price of sensi masks began to rise. The results of the keyword correlation test for “sensi mask”, “cloth mask” and “N-95 mask” show that the keyword search results on Google trended a decline in line with the increase in COVID-19 cases in Indonesia. Public interest in tracing increased at the beginning of COVID-19 entering Indonesia. However, the interest in this search continues to decline and is inversely proportional to the increase in the incidence of COVID-19 cases in Indonesia. Keywords: COVID-19, sensi masks, medical masks, cloth masks, N-95 masks, Google trends

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Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

Applied Computer Systems ◽

10.2478/acss-2020-0004 ◽

2020 ◽

Vol 25 (1) ◽

pp. 33-42

Author(s):

Isaac Kofi Nti ◽

Adebayo Felix Adekoya ◽

Benjamin Asubam Weyori

Keyword(s):

Stock Market ◽

Stock Price ◽

Prediction Accuracy ◽

Time Window ◽

Market Price ◽

Investor Sentiment ◽

Google Trends ◽

Price Movement ◽

The Future ◽

Web News

AbstractPredicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.

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