Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315461
A. Sultoni, L. Hanafi, Zaenal Panutup Aji
Maximum Power Point Tracking (MPPT) is used to find and maintain the maximum power output from photovoltaic array due to weather changing. MPPT works by observe the power located at peak or not, by add disturbance signal. In this paper, We design fuzzy-PID MPPT charge controller for 1.75 KWP photovoltaic system with stand alone configuration. The array consists of 6 modules @280kWP in series configuration. Output power is compared with existing MPPT charge controller. Result shows that the proposed design increases PDC 14% compare to existing MPPT charge controller.
最大功率点跟踪(Maximum Power Point Tracking, MPPT)用于寻找并保持光伏阵列在天气变化时的最大功率输出。MPPT的工作原理是观察功率是否处于峰值位置，加入干扰信号。本文针对单机配置的1.75 KWP光伏系统，设计了模糊pid MPPT充电控制器。该阵列由6个@280kWP模块串联配置而成。输出功率与现有的MPPT充电控制器进行了比较。结果表明，与现有的MPPT充电控制器相比，所设计的充电控制器的PDC值提高了14%。
{"title":"Implementation of Fuzzy-PID Based MPPT for Stand Alone 1.75 kWP PV System","authors":"A. Sultoni, L. Hanafi, Zaenal Panutup Aji","doi":"10.1109/ISRITI51436.2020.9315461","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315461","url":null,"abstract":"Maximum Power Point Tracking (MPPT) is used to find and maintain the maximum power output from photovoltaic array due to weather changing. MPPT works by observe the power located at peak or not, by add disturbance signal. In this paper, We design fuzzy-PID MPPT charge controller for 1.75 KWP photovoltaic system with stand alone configuration. The array consists of 6 modules @280kWP in series configuration. Output power is compared with existing MPPT charge controller. Result shows that the proposed design increases PDC 14% compare to existing MPPT charge controller.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114231862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315417
Radityo Putro Wibisono, P. Rusmin, S. Notodarmojo
Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected internet connectivity, while Artificial Intelligence is intelligence that is created and inserted into a machine to do work like humans do. In this study, a prototype system was built that can control the requirement of chemicals for the coagulation process based on measuring the quality of raw water in the PDAM intake channel using several sensor parameters including Turbidity, pH, and Dissolved Oxygen (DO), by utilizing Internet of Things (IoT) communication. System processing is carried out by a microcontroller. The Artificial Neural Network (ANN) method is used to determine the amount of chemicals or coagulants required for the coagulation process based on raw water quality data, this system can also display raw water quality monitoring data from all sensor parameters used and the status of the dosing pump by IoT communications. By using this system, the amount of doses of chemicals or coagulants used in coagulation through the intake will be more optimal when compared to the current system.
{"title":"Optimization Coagulation Process of Water Treatment Plant Using Neural Network and Internet of Things (IoT) Communication","authors":"Radityo Putro Wibisono, P. Rusmin, S. Notodarmojo","doi":"10.1109/ISRITI51436.2020.9315417","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315417","url":null,"abstract":"Internet of Things (IoT) is a concept that aims to expand the benefits of continuously connected internet connectivity, while Artificial Intelligence is intelligence that is created and inserted into a machine to do work like humans do. In this study, a prototype system was built that can control the requirement of chemicals for the coagulation process based on measuring the quality of raw water in the PDAM intake channel using several sensor parameters including Turbidity, pH, and Dissolved Oxygen (DO), by utilizing Internet of Things (IoT) communication. System processing is carried out by a microcontroller. The Artificial Neural Network (ANN) method is used to determine the amount of chemicals or coagulants required for the coagulation process based on raw water quality data, this system can also display raw water quality monitoring data from all sensor parameters used and the status of the dosing pump by IoT communications. By using this system, the amount of doses of chemicals or coagulants used in coagulation through the intake will be more optimal when compared to the current system.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126137791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315332
Yenni Astuti, Risanuri Hidayat, Agus Bejo
This paper compares the performance of speaker identification systems based on feature extraction methods. Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficient (MFCC) and Discrete Wavelet Transform (DWT) are three of chosen feature extraction techniques used to test. These methods are applied to identify speakers by a word spoken. The system used Dynamic Time Warping (DTW) as classifier. Programming is done on MATLAB for training and testing. In this experiment, the combination of DWT and DTW gives better accuracy result than the other methods.
{"title":"Comparison of Feature Extraction for Speaker Identification System","authors":"Yenni Astuti, Risanuri Hidayat, Agus Bejo","doi":"10.1109/ISRITI51436.2020.9315332","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315332","url":null,"abstract":"This paper compares the performance of speaker identification systems based on feature extraction methods. Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficient (MFCC) and Discrete Wavelet Transform (DWT) are three of chosen feature extraction techniques used to test. These methods are applied to identify speakers by a word spoken. The system used Dynamic Time Warping (DTW) as classifier. Programming is done on MATLAB for training and testing. In this experiment, the combination of DWT and DTW gives better accuracy result than the other methods.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130265640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315364
T. Hermawan, Y. Suryanto, Fahdiaz Alief, Linda Roselina
This research discusses mobile phone forensics on the unsend message feature of social media. It assists investigators forensic or law enforcers in Indonesia to get digital evidence of cybercrime problems such as hoaxes, cyberbullying, illegal transactions, online protection, or other crimes on social media. This research uses Universal Forensic Extraction Device (UFED) and MOBILedit tools to get digital evidence. The selected social media that will be investigated by investigator forensic are Instagram, Line, Whatsapp, Facebook Messenger, Skype, Snapchat, Viber, and Telegram. Based on the results obtained, artifacts can only be found by UFED on social media such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram, whereas digital evidence can not be found on social media such as Line and Snapchat.
{"title":"Android Forensic Tools Analysis for Unsend Chat on Social Media","authors":"T. Hermawan, Y. Suryanto, Fahdiaz Alief, Linda Roselina","doi":"10.1109/ISRITI51436.2020.9315364","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315364","url":null,"abstract":"This research discusses mobile phone forensics on the unsend message feature of social media. It assists investigators forensic or law enforcers in Indonesia to get digital evidence of cybercrime problems such as hoaxes, cyberbullying, illegal transactions, online protection, or other crimes on social media. This research uses Universal Forensic Extraction Device (UFED) and MOBILedit tools to get digital evidence. The selected social media that will be investigated by investigator forensic are Instagram, Line, Whatsapp, Facebook Messenger, Skype, Snapchat, Viber, and Telegram. Based on the results obtained, artifacts can only be found by UFED on social media such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram, whereas digital evidence can not be found on social media such as Line and Snapchat.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315421
Nomarhinta Solihah, M. I. Nashiruddin, Eliandri Shintani Wulandari
Video services on Passive Optical Networks (PON) are widely used for the IPTV multicast services and proliferating. One of the new PON technologies used by telecommunication operators in Indonesia is the 10-Gigabit-capable symmetric passive optical network (XGS-PON). However, there is no technical standardization related to multicast services on the XGS-PON system. Therefore, this study will develop a test method and evaluate multicast services' performance on Optical Line Termination (OLT) equipment of XGS-PON. The OLT has the most critical function in managing services, including distributing Internet Protocol Television (IPTV) service to customers. Six parameters proposed as standardization regulation to determine the XGS-PON OLT capability are as follows: IGMP version 2, IGMP version 3, IGMP Proxy, IGMP transparent snooping, IGMP snooping with proxy reporting, and IGMP Multicast Group. The experiment result confirmed that XGS-PON OLT supports multicast protocols following ITU-T G.9807.1 recommendations, namely IGMP version 2 and IGMP version 3. It also shows that XGS-PON OLT supports IGMP mode capabilities such as IGMP proxy mode, IGMP transparent snooping mode, and IGMP snooping with proxy reporting. Furthermore, XGS-PON OLT supports the maximum number of multicast groups simultaneously for 2048 IGMP multicast groups following the TR-101 guideline from Broadband Forum. These results can be used as a reference for technical standardization regulations development of IPTV multicast service in XGS-PON.
无源光网络(PON)上的视频业务在IPTV组播业务中得到了广泛的应用并呈激增趋势。印尼电信运营商使用的一种新型PON技术是10千兆对称无源光网络(XGS-PON)。但是，在XGS-PON系统上没有与组播业务相关的技术标准化。因此，本研究将开发一种测试方法并评估多播业务在XGS-PON光线路终端(OLT)设备上的性能。OLT在业务管理中具有最关键的功能，包括向客户分发IPTV (Internet Protocol Television)业务。作为决定XGS-PON OLT能力的标准化规则，提出了6个参数:IGMP version 2、IGMP version 3、IGMP Proxy、IGMP transparent snooping、IGMP snooping with Proxy reporting、IGMP Multicast Group。实验结果证实，XGS-PON OLT支持符合ITU-T G.9807.1推荐的组播协议，即IGMP version 2和IGMP version 3。同时也说明了XGS-PON OLT支持IGMP模式的功能，如IGMP代理模式、IGMP透明snooping模式、带代理报告的IGMP snooping等。此外，XGS-PON OLT还支持2048个IGMP组播组同时组播组的最大数量，遵循宽带论坛TR-101指南。研究结果可为XGS-PON中IPTV组播业务的技术标准化规程制定提供参考。
{"title":"Performance Evaluation of IPTV Multicast Service Testing for XGS-PON Optical Line Termination","authors":"Nomarhinta Solihah, M. I. Nashiruddin, Eliandri Shintani Wulandari","doi":"10.1109/ISRITI51436.2020.9315421","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315421","url":null,"abstract":"Video services on Passive Optical Networks (PON) are widely used for the IPTV multicast services and proliferating. One of the new PON technologies used by telecommunication operators in Indonesia is the 10-Gigabit-capable symmetric passive optical network (XGS-PON). However, there is no technical standardization related to multicast services on the XGS-PON system. Therefore, this study will develop a test method and evaluate multicast services' performance on Optical Line Termination (OLT) equipment of XGS-PON. The OLT has the most critical function in managing services, including distributing Internet Protocol Television (IPTV) service to customers. Six parameters proposed as standardization regulation to determine the XGS-PON OLT capability are as follows: IGMP version 2, IGMP version 3, IGMP Proxy, IGMP transparent snooping, IGMP snooping with proxy reporting, and IGMP Multicast Group. The experiment result confirmed that XGS-PON OLT supports multicast protocols following ITU-T G.9807.1 recommendations, namely IGMP version 2 and IGMP version 3. It also shows that XGS-PON OLT supports IGMP mode capabilities such as IGMP proxy mode, IGMP transparent snooping mode, and IGMP snooping with proxy reporting. Furthermore, XGS-PON OLT supports the maximum number of multicast groups simultaneously for 2048 IGMP multicast groups following the TR-101 guideline from Broadband Forum. These results can be used as a reference for technical standardization regulations development of IPTV multicast service in XGS-PON.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127178515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/isriti51436.2020.9315491
{"title":"Welcome Speech from the Chairman of Stmik Akakom Yogyakarta","authors":"","doi":"10.1109/isriti51436.2020.9315491","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315491","url":null,"abstract":"","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/isriti51436.2020.9315355
{"title":"Index","authors":"","doi":"10.1109/isriti51436.2020.9315355","DOIUrl":"https://doi.org/10.1109/isriti51436.2020.9315355","url":null,"abstract":"","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315397
V. J. L. Engel, Firhat Hidayat, Richard Dwiputra
The selection of features plays a big role in improving the results of a computer network attack detection system. This research used a model of feature selection to find the best combination of network traffic features to identify network attacks while retaining power explanations. This research also used filter-based feature selection, namely Information Gain (IG) and Gain Ratio (GR). Training and testing can be carried out after sigma value of SVM parameter has been determined. From sigma value testing, we chose sigma value of 5000. After SVM training, it is found that Gain Ratio with 30 features perform best for most measurement and classes. Nevertheless, full 41 features outperform IG and GR for probe class. Also, model that integrating feature selection has possibility to converge faster. It is recommended that further analysis and examination is needed to understand features combination result. Additionally, further research is needed to determine the effectiveness of features combinations to improve model performance and to try different approaches besides the filter-based method.
特征的选择对提高计算机网络攻击检测系统的检测效果起着至关重要的作用。本研究使用特征选择模型来寻找网络流量特征的最佳组合，以识别网络攻击，同时保留功率解释。本研究还采用基于滤波器的特征选择，即信息增益(Information Gain, IG)和增益比(Gain Ratio, GR)。确定支持向量机参数的sigma值后，即可进行训练和测试。从sigma值检验中，我们选择sigma值为5000。经过SVM训练，发现增益比为30个特征时，对大多数测量和类的效果最好。然而，对于探针类，41个特性的性能优于IG和GR。此外，集成特征选择的模型有可能收敛得更快。建议进一步分析和检查，以了解特征组合结果。此外，还需要进一步的研究来确定特征组合对提高模型性能的有效性，并尝试除基于滤波器的方法之外的不同方法。
{"title":"Network Attack Detection System Using Filter-based Feature Selection and SVM","authors":"V. J. L. Engel, Firhat Hidayat, Richard Dwiputra","doi":"10.1109/ISRITI51436.2020.9315397","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315397","url":null,"abstract":"The selection of features plays a big role in improving the results of a computer network attack detection system. This research used a model of feature selection to find the best combination of network traffic features to identify network attacks while retaining power explanations. This research also used filter-based feature selection, namely Information Gain (IG) and Gain Ratio (GR). Training and testing can be carried out after sigma value of SVM parameter has been determined. From sigma value testing, we chose sigma value of 5000. After SVM training, it is found that Gain Ratio with 30 features perform best for most measurement and classes. Nevertheless, full 41 features outperform IG and GR for probe class. Also, model that integrating feature selection has possibility to converge faster. It is recommended that further analysis and examination is needed to understand features combination result. Additionally, further research is needed to determine the effectiveness of features combinations to improve model performance and to try different approaches besides the filter-based method.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"29 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129610008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315436
S. Mungkasi
We consider an enzyme-substrate reaction-diffusion problem. Unsteady and steady state models are recalled. For the unsteady state case, the model is in the form of a second order partial differential equation. We solve the unsteady state model using the explicit numerical finite difference method, which is forward difference in time and centered difference in space. For the general steady state case, the model is in the form of a second order ordinary differential equation. We solve the general steady state model using the explicit first order Euler's numerical method. For the particular steady state case of the unsaturated catalytic kinetics, we derive the exact analytical solution using the characteristic method of ordinary differential equations. For the particular steady state case of the saturated catalytic kinetics, we derive the exact analytical solution using the direct-integration method. The obtained exact analytical solutions are identical with the existing exact analytical solutions derived using the variational iteration method. With the aid of computer, the enzyme-substrate reaction-diffusion problem can be solved and simulated successfully for both unsteady and steady state cases.
{"title":"Some Numerical and Analytical Solutions to an Enzyme-Substrate Reaction-Diffusion Problem","authors":"S. Mungkasi","doi":"10.1109/ISRITI51436.2020.9315436","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315436","url":null,"abstract":"We consider an enzyme-substrate reaction-diffusion problem. Unsteady and steady state models are recalled. For the unsteady state case, the model is in the form of a second order partial differential equation. We solve the unsteady state model using the explicit numerical finite difference method, which is forward difference in time and centered difference in space. For the general steady state case, the model is in the form of a second order ordinary differential equation. We solve the general steady state model using the explicit first order Euler's numerical method. For the particular steady state case of the unsaturated catalytic kinetics, we derive the exact analytical solution using the characteristic method of ordinary differential equations. For the particular steady state case of the saturated catalytic kinetics, we derive the exact analytical solution using the direct-integration method. The obtained exact analytical solutions are identical with the existing exact analytical solutions derived using the variational iteration method. With the aid of computer, the enzyme-substrate reaction-diffusion problem can be solved and simulated successfully for both unsteady and steady state cases.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"528 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116490620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-10DOI: 10.1109/ISRITI51436.2020.9315414
Manlika Ratchagit, B. Wiwatanapataphee, D. Nur
This paper aims to present how to estimate a model parameter, namely the fixed rate of the investment return in the stochastic delay difference equation in financial time series using the two m-delay autoregressive coefficients. The autoregressive coefficients (ARC) algorithm is proposed and compares with the classical differential evolution (DE) algorithm. For a Monte-Carlo simulation tool, the results obtained from the model with the estimated parameter are validated with historical financial data of IBEX 35, JPM and GOOG from Thomson Reuters database in the period between 2008 and 2010. The numerical results confirm that the two $m$-delay autoregressive coefficients perform well to estimate the fixed rate of the investment return and reduce the computation time for the matching process.
{"title":"On Parameter Estimation of Stochastic Delay Difference Equation using the Two $m$-delay Autoregressive Coefficients","authors":"Manlika Ratchagit, B. Wiwatanapataphee, D. Nur","doi":"10.1109/ISRITI51436.2020.9315414","DOIUrl":"https://doi.org/10.1109/ISRITI51436.2020.9315414","url":null,"abstract":"This paper aims to present how to estimate a model parameter, namely the fixed rate of the investment return in the stochastic delay difference equation in financial time series using the two m-delay autoregressive coefficients. The autoregressive coefficients (ARC) algorithm is proposed and compares with the classical differential evolution (DE) algorithm. For a Monte-Carlo simulation tool, the results obtained from the model with the estimated parameter are validated with historical financial data of IBEX 35, JPM and GOOG from Thomson Reuters database in the period between 2008 and 2010. The numerical results confirm that the two $m$-delay autoregressive coefficients perform well to estimate the fixed rate of the investment return and reduce the computation time for the matching process.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125216606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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