scholarly journals Network Bandwidth Utilization Prediction Based on Observed SNMP Data

2018 ◽  
Vol 13 (1) ◽  
pp. 160-168
Author(s):  
Nandalal Rana ◽  
Krishna P Bhandari ◽  
Surendra Shrestha
Keyword(s):  
Linear Time ◽  
Arima Model ◽  
Real Data ◽  
Service Planning ◽  
Bandwidth Utilization ◽  
Network Nodes ◽  
Network Bandwidth ◽  
Bandwidth Requirement ◽  
Logit Transformation

 Bandwidth requirement prediction is an important part of network design and service planning. The natural way of predicting bandwidth requirement for existing network is to analyze the past trends and apply appropriate mathematical model to predict for the future. For this research, the historical usage data of FWDR network nodes of Nepal Telecom is subject to univariate linear time series ARIMA model after logit transformation to predict future bandwidth requirement. The predicted data is compared to the real data obtained from the same network and the predicted data has been found to be within 10% MAPE. This model reduces the MAPE by 11.71% and 15.42% respectively as compared to the non-logit transformed ARIMA model at 99% CI. The results imply that the logit transformed ARIMA model has better performance compared to non-logit-transformed ARIMA model. For more accurate and longer term predictions, larger dataset can be taken along with season adjustments and consideration of long term variations.Journal of the Institute of Engineering, 2017, 13(1): 160-168

PREDICTING CHARACTERISTICS OF SELF SIMILAR TRAFFIC BY SPLINE-EXTRAPOLATION

2019 ◽  
Author(s):  
Irina Strelkovskay ◽  
Irina Solovskaya ◽  
Anastasija Makoganjuk ◽  
Nikolaj Severin
Keyword(s):  
Quality Characteristics ◽  
Traffic Prediction ◽  
Normative Values ◽  
Spline Functions ◽  
Network Nodes ◽  
Traffic Forecasting ◽  
Network Equipment ◽  
Self Similar ◽  
Accuracy Of Prediction

The problem of forecasting self-similar traffic, which is characterized by a considerable number of ripples and the property of long-term dependence, is considered. It is proposed to use the method of spline extrapolation using linear and cubic splines. The results of self-similar traffic prediction were obtained, which will allow to predict the necessary size of the buffer devices of the network nodes in order to avoid congestion in the network and exceed the normative values ​​of QoS quality characteristics. The solution of the problem of self-similar traffic forecasting obtained with the Simulink software package in Matlab environment is considered. A method of extrapolation based on spline functions is developed. The proposed method has several advantages over the known methods, first of all, it is sufficient ease of implementation, low resource intensity and accuracy of prediction, which can be enhanced by the use of quadratic or cubic interpolation spline functions. Using the method of spline extrapolation, the results of self-similar traffic prediction were obtained, which will allow to predict the required volume of buffer devices, thereby avoiding network congestion and exceeding the normative values ​​of QoS quality characteristics. Given that self-similar traffic is characterized by the presence of "bursts" and a long-term dependence between the moments of receipt of applications in this study, given predetermined data to improve the prediction accuracy, it is possible to use extrapolation based on wavelet functions, the so-called wavelet-extrapolation method. Based on the results of traffic forecasting, taking into account the maximum values ​​of network node traffic, you can give practical guidance on how traffic is redistributed across the network. This will balance the load of network objects and increase the efficiency of network equipment.

Multivariate linear time series models

1984 ◽  
Vol 16 (3) ◽  
pp. 492-561 ◽  
Author(s):  
E. J. Hannan ◽  
L. Kavalieris
Keyword(s):  
Limit Theorems ◽  
Analysis Of Algorithms ◽  
Linear Time ◽  
Asymptotic Properties ◽  
Real Data ◽  
Rates Of Convergence ◽  
Structure Theory ◽  
Time Series Models ◽  
Transfer Model ◽  
Rational Transfer

This paper is in three parts. The first deals with the algebraic and topological structure of spaces of rational transfer function linear systems—ARMAX systems, as they have been called. This structure theory is dominated by the concept of a space of systems of order, or McMillan degree, n, because of the fact that this space, M(n), can be realised as a kind of high-dimensional algebraic surface of dimension n(2s + m) where s and m are the numbers of outputs and inputs. In principle, therefore, the fitting of a rational transfer model to data can be considered as the problem of determining n and then the appropriate element of M(n). However, the fact that M(n) appears to need a large number of coordinate neighbourhoods to cover it complicates the task. The problems associated with this program, as well as theory necessary for the analysis of algorithms to carry out aspects of the program, are also discussed in this first part of the paper, Sections 1 and 2.The second part, Sections 3 and 4, deals with algorithms to carry out the fitting of a model and exhibits these algorithms through simulations and the analysis of real data.The third part of the paper discusses the asymptotic properties of the algorithm. These properties depend on uniform rates of convergence being established for covariances up to some lag increasing indefinitely with the length of record, T. The necessary limit theorems and the analysis of the algorithms are given in Section 5. Many of these results are of interest independent of the algorithms being studied.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

scholarly journals Self-Assisted First-Fix Method for A-BDS Receivers with Medium- and Long-Term Ephemeris Extension

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zilong Shen ◽  
Jing Peng ◽  
Wenxiang Liu ◽  
Feixue Wang ◽  
Shibing Zhu ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Rapid Development ◽  
Real Data ◽  
Satellite System ◽  
Time Interval ◽  
Signal Acquisition ◽  
Positioning Accuracy ◽  
Broadcast Ephemeris ◽  
Intelligent Logistics

As a sensor for standalone position and velocity determination, the BeiDou Navigation Satellite System (BDS) receiver is becoming an important part of the intelligent logistics systems under rapid development in China. The applications in the mass market urgently require the BDS receivers to improve the performance of such functions, that is, shorter Time to First Fix (TTFF) and faster navigation signal acquisition speed with Ephemeris Extension (EE) in standalone mode. As a practical way to improve such functions of the Assisted BDS (A-BDS) receivers without the need for specialized hardware support, a Self-Assisted First-Fix (SAFF) method with medium- and long-term EE is proposed in this paper. In this SAFF method, the dynamic Medium- and Long-Term Orbit Prediction (MLTOP) method, which uses the historical broadcast ephemeris data with the optimal configuration of the dynamic models and orbit fitting time interval, is utilized to generate the extended ephemeris. To demonstrate the performance of the MLTOP method used in the SAFF method, a suit of tests, which were based on the real data of broadcast ephemeris and precise ephemeris, were carried out. In terms of the positioning accuracy, the overall performance of the SAFF method is illustrated. Based on the characteristics of the medium- and long-term EE, the simulation tests for the SAFF method were conducted. Results show that, for the SAFF method with medium- and long-term EE of the BeiDou MEO/IGSO satellites, the horizontal positioning accuracy is about 12 meters, and the overall positioning accuracy is about 25 meters. The results also indicate that, for the BeiDou satellites with different orbit types, the optimal configurations of the MLTOP method are different.

A Mixture Price Trend Model for Long-Term Risk Management

2010 ◽  
pp. 157-173
Author(s):  
Eric S. Fung ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Estimation Method ◽  
Real Data ◽  
Price Trend ◽  
Trend Model ◽  
Proposed Model ◽  
Price Trends ◽  
Long Term Forecasting

In financial forecasting, a long-standing challenging issue is to develop an appropriate model for forecasting long-term risk management of enterprises. In this chapter, using financial markets as an example, we introduce a mixture price trend model for long-term forecasts of financial asset prices with a view to applying it for long-term financial risk management. The key idea of the mixture price trend model is to provide a general and flexible way to incorporate various price trend behaviors and to extract information from price trends for long-term forecasting. Indeed, the mixture price trend model can incorporate model uncertainty in the price trend model, which is a key element for risk management and is overlooked in some of the current literatures. The mixture price trend model also allows the incorporation of users’ subjective views on long-term price trends. An efficient estimation method is introduced. Statistical analysis of the proposed model based on real data will be conducted to illustrate the performance of the model.

scholarly journals A System Dynamics Model of Online Stores’ Sales: Positive and Negative E-WOM and Promotion Perspective

2019 ◽  
Vol 11 (21) ◽  
pp. 6045 ◽  
Author(s):  
Qiang Yan ◽  
Simin Zhou ◽  
Xiaoyan Zhang ◽  
Ye Li
Keyword(s):  
System Dynamics ◽  
Real Data ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Sustainable Operations ◽  
Main Research ◽  
Interaction Diagram ◽  
Online Stores ◽  
The Impact

In this paper, we build a causal interaction diagram between the factors that may influence the sales and profits of online stores. An online store’s real operation data were used to help determine the causal relationship between variables. Finally, we proposed a system dynamics model and conducted a simulation of the operation of an online store. In this model, we focused on the impact of promotion and positive/negative electronic word of mouth (e-WOM) on the sales and profits of the online stores. The simulation results showed a similar trend to the real data and the main research finding showed that promotion is not a long-term measure for the sustainable development of online stores. Excessive promotion effort may lead to consumers’ dissatisfaction leading the increase of negative e-WOM. The systematic simulation can help us understand better the long-term effect of promotion and e-WOM on the operation of online stores. Finally, we gave some management suggestions for online stores’ sustainable operations.

Multivariate linear time series models

1984 ◽  
Vol 16 (03) ◽  
pp. 492-561 ◽  
Author(s):  
E. J. Hannan ◽  
L. Kavalieris
Keyword(s):  
Limit Theorems ◽  
Analysis Of Algorithms ◽  
Linear Time ◽  
Asymptotic Properties ◽  
Real Data ◽  
Rates Of Convergence ◽  
Structure Theory ◽  
Time Series Models ◽  
Transfer Model ◽  
Rational Transfer

This paper is in three parts. The first deals with the algebraic and topological structure of spaces of rational transfer function linear systems—ARMAX systems, as they have been called. This structure theory is dominated by the concept of a space of systems of order, or McMillan degree,n,because of the fact that this space,M(n), can be realised as a kind of high-dimensional algebraic surface of dimensionn(2s+m) wheresandmare the numbers of outputs and inputs. In principle, therefore, the fitting of a rational transfer model to data can be considered as the problem of determiningnand then the appropriate element ofM(n). However, the fact thatM(n) appears to need a large number of coordinate neighbourhoods to cover it complicates the task. The problems associated with this program, as well as theory necessary for the analysis of algorithms to carry out aspects of the program, are also discussed in this first part of the paper, Sections 1 and 2.The second part, Sections 3 and 4, deals with algorithms to carry out the fitting of a model and exhibits these algorithms through simulations and the analysis of real data.The third part of the paper discusses the asymptotic properties of the algorithm. These properties depend on uniform rates of convergence being established for covariances up to some lag increasing indefinitely with the length of record,T. The necessary limit theorems and the analysis of the algorithms are given in Section 5. Many of these results are of interest independent of the algorithms being studied.

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