scholarly journals Homicídios em homens jovens: tendência e projeção em Mato Grosso-Brasil, 1996-2022

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Franciele Silvia Carlo ◽  
Ligia Regina Oliveira ◽  
Amanda Cristina Souza Andrade
Keyword(s):  
Moving Average ◽  
Mato Grosso ◽  
Autoregressive Integrated Moving Average

Objetivo: Analisar a tendência de 1996 a 2017 e projetar de 2018 a 2022 a taxa de homicídios em homens jovens, segundo faixa etária, no estado de Mato Grosso. Métodos: Trata-se de Estudo ecológico, exploratório, de tendência temporal. Para modelagem da série e predição da taxa de homicídios para o período de 2018-2022, utilizou-se o método ARIMA (autoregressive integrated moving average). Resultados: Verificou-se a tendência temporal crescente de homicídios em homens jovens (15 a 29 anos), com manutenção de taxas altas para a população masculina de 15 a 19 anos. A previsão para o quinquênio 2018-2022 indicou elevação das taxas de homicídios entre homens jovens, revelando crescimento maior em adolescentes, mesmo com menores taxas ao longo dos anos. Conclusão: Este estudo indicou tendência crescente de homicídios em homens jovens em Mato Grosso, sobretudo em adolescentes. Visto que o homicídio possui variadas facetas fomentadoras admite-se que número substancial de homicídios deste estrato social poderia ser evitado seguindo estratégias eficazes de prevenção por meio de políticas públicas.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

Hugh Jackman vs. Skin Cancer Awareness Month: Promoting Public Interest in Basal Cell Carcinoma (Preprint)

2020 ◽  
Author(s):  
Trevor Torgerson ◽  
Jennifer Austin ◽  
Jam Khojasteh ◽  
Matt Vassar
Keyword(s):  
Social Media ◽  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Moving Average ◽  
Public Awareness ◽  
Sun Exposure ◽  
Cancer Awareness ◽  
Autoregressive Integrated Moving Average

BACKGROUND Public awareness for BCC is particularly important, as its major risk factors — increased sun exposure and number of sunburns — are largely preventable. OBJECTIVE Determine whether social media posts from celebrities has an affect on public awareness of basal cell carcinoma. METHODS We used Google Trends to investigate whether public awareness for basal cell carcinoma (BCC) increased following social media posts from Hugh Jackman. To forecast the expected search interest for BCC, melanoma and sunscreen in the event that each celebrity had not posted on social media, we used the autoregressive integrated moving average (ARIMA) algorithm. RESULTS We found that social media posts from Hugh Jackman, a well-known actor, increased relative search interest above the expected search interest calculated using an ARIMA forecasting model. CONCLUSIONS Our results also suggest that increasing awareness by Skin Cancer Awareness Month may be less effective for BCC, but a celebrity spokesperson has the potential to increase awareness. BCC is largely preventable, so increasing awareness could lead to a decrease in incidence.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

scholarly journals Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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