Validation of High-Fidelity Traffic Simulation Models

2000 ◽  
Vol 1710 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Lei Rao ◽  
Larry Owen
Keyword(s):  
Traffic Simulation ◽  
Statistical Tests ◽  
Moving Average ◽  
Simulation Models ◽  
Output Data ◽  
Autoregressive Integrated Moving Average ◽  
Simulation Output ◽  
Validation Procedure ◽  
Arima Modeling ◽  
Operational Validation

A multistage validation framework that accounts for the realistic nature of traffic simulation output data is proposed. The framework consists of conceptual validation and operational validation. The operational validation comprises a qualitative approach, which involves static and animated Turing tests, and a quantitative approach, which involves three levels of statistical tests. Particularly in the third-level statistical test, the autocorrelation and nonstationary nature of traffic simulation output data is emphasized, its implications on validation methods are explored, and a univariate nonseasonal autoregressive-integrated-moving-average (ARIMA) modeling approach is proposed. Finally, numerical results for an actual freeway network are presented. The validation results illustrate that the proposed multistage validation procedure can account for the complexity of the validation task and its conclusions.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model

2021 ◽  
pp. 22-35
Author(s):  
Dmytro Chumachenko ◽  
Ievgen Meniailov ◽  
Andrii Hrimov ◽  
Vladislav Lopatka ◽  
Olha Moroz ◽  
...  
Keyword(s):  
Process Simulation ◽  
Respiratory Infections ◽  
Moving Average ◽  
Simulation Models ◽  
Influenza Epidemic ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Epidemic Process ◽  
Moving Average Model ◽  
Control And Prevention

Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.

Examining the Influence of Jessica’s Law on Reported Forcible Rape

2016 ◽  
Vol 28 (1) ◽  
pp. 87-101 ◽  
Author(s):  
Rick Dierenfeldt ◽  
Jennifer Varriale Carson
Keyword(s):  
Sex Offenders ◽  
Sex Offender ◽  
Electronic Monitoring ◽  
Sexual Offending ◽  
Moving Average ◽  
Policy Implications ◽  
State Governments ◽  
Autoregressive Integrated Moving Average ◽  
Arima Modeling ◽  
The Impact

Since the 1990s, several measures intended to deter sexual offending have been instituted by state governments. A recent example is Jessica’s Law. First adopted in Florida, variations of Jessica’s Law have since been enacted by the majority of states. The impact of this legislation on forcible rape remains unexplored. Using a general deterrence framework, we apply Autoregressive Integrated Moving Average (ARIMA) modeling to monthly Uniform Crime Report (UCR) aggregations of reported forcible rape from 2000 to 2011 in states requiring lifetime electronic monitoring of convicted sex offenders as a condition of Jessica’s Law. Results indicate a null relationship between Jessica’s Law and reported forcible rape. Policy implications related to the efficacy of sex offender legislation and alternatives for reducing sexual offending are discussed.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals Previsão da exportação brasileira de açúcar avaliada por meio de gráficos de controle de resíduos

2020 ◽  
Vol 42 ◽  
pp. e39
Author(s):  
Valentina Wolff Lirio ◽  
Renan Mitsuo Ueda ◽  
Bianca Reichert ◽  
Adriano Mendonça Souza
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Sugar Production ◽  
Arima Models ◽  
Seasonal Behavior ◽  
Autoregressive Integrated Moving Average ◽  
Residual Control Charts ◽  
Arima Modeling ◽  
Residual Control ◽  
Sugar Exports

Sugar production and exportation are important factors for the Brazilian economy, because Brazil produces the largest amount of sugar and accounts for almost half of the world´s sugar exports. This research aimed to monitor the sugar export from January 2000 to April 2019, by means of residual control charts with pretreatment of autoregressive integrated moving average (ARIMA) models. The data used in the study were collected from the Portal Única website. We opted for the application of ARIMA modeling because the data was not stationarity and presented autocorrelated values. The best model to predict the Brazilian sugar exports was SARIMA (1,1,1)(1,0,1)6 due to the seasonal behavior of the series, which may be related to the sugarcane planting and harvesting period. It was possible to observe the presence of upper-limit outliers in the residual control chart, in October 2012 and February 2016, which characterize a sugar exports higher than forecasted exports.

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

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