Forecasting the Search Trend of Muslim Clothing in Indonesia on Google Trends Data Using ARIMAX and Neural Network

2019 ◽  
pp. 272-286
Author(s):  
Novri Suhermi ◽  
Suhartono ◽  
Regita Putri Permata ◽  
Santi Puteri Rahayu
Keyword(s):  
Neural Network ◽  
Google Trends

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

scholarly journals The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland

2019 ◽  
Vol 16 (17) ◽  
pp. 3201 ◽  
Author(s):  
Joana M. Barros ◽  
Ruth Melia ◽  
Kady Francis ◽  
John Bogue ◽  
Mary O’Sullivan ◽  
...  
Keyword(s):  
Neural Network ◽  
United Kingdom ◽  
Suicide Risk ◽  
Google Trends ◽  
Search Rate ◽  
Network Approach ◽  
Search Query ◽  
Neural Network Approach ◽  
Suicide Rates ◽  
Search Terms

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

scholarly journals COVID-19 Mass Media Infodemic in Six European Countries

2020 ◽  
Author(s):  
Kejo Starosta ◽  
Cristian Bogdan Onete ◽  
Raluca Grosu ◽  
Doru Plesea
Keyword(s):  
Neural Network ◽  
Mass Media ◽  
Two Dimensions ◽  
Time Span ◽  
Google Trends ◽  
Media Reporting ◽  
The United Kingdom ◽  
Actual Reality ◽  
Media Reports ◽  
News Sentiment

Our paper tackles the development of media reporting during the COVID-19 pandemic, focusing on the January - November 2020 time span, in France, Germany, Romania, Spain, Switzerland, and the United Kingdom. We aim to make media reporting transparent on two dimensions: the coverage of COVID-19-related topics and the negativity of the COVID-19 media reporting. To achieve this goal, we analysed a large news dataset with 841,415 pieces of news—including 202,608 COVID-19 media reports—on an LSTM neural network. The news sentiment data and the corresponding coverage are set in relation to the WHO data on COVID-19 and to Google Trends. This compares the reality, that is WHO data, the perceived and reported reality, that is news data, and the actions based on the perceived and the actual reality, that is Google Trends. The results show that media reporting on COVID-19 is unprecedented in terms of coverage and negativity. Furthermore, the study quantifies how far media reporting detached from the facts after the first wave of COVID-19 and how an Infodemic spread across Europe.<br>

scholarly journals Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

2019 ◽  
Vol 8 (4) ◽  
pp. 111 ◽  
Author(s):  
Emmanuel Silva ◽  
Hossein Hassani ◽  
Dag Madsen ◽  
Liz Gee
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Spectrum Analysis ◽  
Consumer Behaviour ◽  
Singular Spectrum Analysis ◽  
Academic Research ◽  
Google Trends ◽  
Hybrid Neural Network ◽  
Singular Spectrum

This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.

scholarly journals COVID-19 Mass Media Infodemic in Six European Countries

2020 ◽  
Author(s):  
Kejo Starosta ◽  
Cristian Bogdan Onete ◽  
Raluca Grosu ◽  
Doru Plesea
Keyword(s):  
Neural Network ◽  
Mass Media ◽  
Two Dimensions ◽  
Time Span ◽  
Google Trends ◽  
Media Reporting ◽  
The United Kingdom ◽  
Actual Reality ◽  
Media Reports ◽  
News Sentiment

Our paper tackles the development of media reporting during the COVID-19 pandemic, focusing on the January - November 2020 time span, in France, Germany, Romania, Spain, Switzerland, and the United Kingdom. We aim to make media reporting transparent on two dimensions: the coverage of COVID-19-related topics and the negativity of the COVID-19 media reporting. To achieve this goal, we analysed a large news dataset with 841,415 pieces of news—including 202,608 COVID-19 media reports—on an LSTM neural network. The news sentiment data and the corresponding coverage are set in relation to the WHO data on COVID-19 and to Google Trends. This compares the reality, that is WHO data, the perceived and reported reality, that is news data, and the actions based on the perceived and the actual reality, that is Google Trends. The results show that media reporting on COVID-19 is unprecedented in terms of coverage and negativity. Furthermore, the study quantifies how far media reporting detached from the facts after the first wave of COVID-19 and how an Infodemic spread across Europe.<br>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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