scholarly journals Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries (Preprint)

10.2196/34016 ◽  
2021 ◽  
Author(s):  
Kazuya Taira ◽  
Rikuya Hosokawa ◽  
Tomoya Itatani ◽  
Sumio Fujita
Keyword(s):  
Time Series ◽  
Vector Autoregression ◽  
Time Series Model ◽  
Internet Search ◽  
Search Queries

Predicting the Number of Suicides in Japan: A Vector Autoregression Time Series Model Using Internet Search Queries (Preprint)

2021 ◽  
Author(s):  
Kazuya Taira ◽  
Rikuya Hosokawa ◽  
Tomoya Itatani ◽  
Sumio Fujita
Keyword(s):  
Granger Causality ◽  
Vector Autoregression ◽  
Unit Roots ◽  
Granger Causality Test ◽  
Lagrangian Multiplier ◽  
Internet Search ◽  
Causality Test ◽  
Search Queries ◽  
Original Series ◽  
Lm Test

BACKGROUND The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is critical to take timely preventive measures. OBJECTIVE In this study, we examine whether the number and characteristics of suicides can be predicted based on the Internet search behavior and the search queries. METHODS The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender on the queries associated with "suicide" on "Yahoo Search" from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to "suicide" were searched before searching for the keyword "suicide," and extracted and used for analyses: "abuse," "work, don’t want to go," "company, want to quit," "divorce," and "no money." The Augmented Dickey–Fuller and Johansen's tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch–Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque–Bera (JB) test were employed to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. RESULTS In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24, max tau 3: −5.38) and women (minimum tau 3: −9.24, max tau 3: −5.38) had no unit roots for all variables. In Johansen's test, a cointegration relationship was observed among several variables. The queries used in the converged models were "divorce" for men (BG-LM test: p= 0.55; ARCH-LM test: p= 0.63; JB test: p= 0.66) and "no money" for women (BG-LM test: p = 0.17; ARCH-LM test: p = 0.15; JB test: p= 0.10). In the Granger causality test for each variable, "divorce" was significant for both men (F= 3.29, p = 0.041) and women (F = 3.23, p = 0.044). ¬ CONCLUSIONS The number of suicides can be predicted by the search queries related to the keyword "suicide." Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on "no money" and "divorce" predict suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

scholarly journals Estimation of asthma symptom onset using Internet search queries: A lag-time series analysis (Preprint)

2020 ◽  
Author(s):  
Yulin Hswen ◽  
Amanda Zhang ◽  
Bruno Ventelou
Keyword(s):  
Time Series ◽  
Hospital Admission ◽  
Symptom Onset ◽  
Hospital Admissions ◽  
Pearson Correlation ◽  
Internet Search ◽  
Search Queries ◽  
Risk Of Death ◽  
Search Volume ◽  
Google Search

BACKGROUND Asthma affects over 330 million people worldwide. Timing of the asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. OBJECTIVE This study evaluates the utility of the Internet search query data for the identification the onset of asthma symptoms. METHODS Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks prior to hospital admission to 4 weeks after hospital admission. RESULTS Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. CONCLUSIONS Our findings demonstration Internet search queries can earlier predict asthma events and may be a better use for classifying the measurement of timing of symptom onset.

Forecasting Short-Term Housing Transaction Volumes using Time-Series and Internet Search Queries

2019 ◽  
Vol 23 (6) ◽  
pp. 2409-2416 ◽  
Author(s):  
Kanghyeok Lee ◽  
Hanbeen Kim ◽  
Do Hyoung Shin
Keyword(s):  
Time Series ◽  
Internet Search ◽  
Short Term ◽  
Search Queries

scholarly journals Suicide‐related Internet search queries in India following media reports of a celebrity suicide: an interrupted time series analysis

2021 ◽  
Vol 20 (1) ◽  
pp. 143-144
Author(s):  
Gregory Armstrong ◽  
Tilahun Haregu ◽  
Vikas Arya ◽  
Lakshmi Vijayakumar ◽  
Mark Sinyor ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Internet Search ◽  
Interrupted Time Series Analysis ◽  
Search Queries ◽  
Series Analysis ◽  
Celebrity Suicide ◽  
Media Reports

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series

Time Series Model of Wind Power Forecasting Error by using Beta Distribution for Optimal Sizing of Battery Storage

2019 ◽  
Vol 139 (3) ◽  
pp. 212-224
Author(s):  
Xiaowei Dui ◽  
Masakazu Ito ◽  
Yu Fujimoto ◽  
Yasuhiro Hayashi ◽  
Guiping Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Beta Distribution ◽  
Time Series Model ◽  
Battery Storage ◽  
Wind Power Forecasting ◽  
Optimal Sizing ◽  
Forecasting Error ◽  
Power Forecasting
Sign in / Sign up

Export Citation Format

Share Document