ANFIS method for forecasting internet traffic time series

Chabaa, Samira; Zeroual, Abdelouhab; Antari, Jilali

doi:10.1109/mms.2009.5409834

Cited by 20 publications

(10 citation statements)

References 11 publications

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“…ANFIS has been applied in several forecasting domains such as electricity price forecasting [11], weather forecasting [12], solar radiation data forecasting [13], daily stream flow forecasting [14], internet traffic time series forecasting [15] and demand forecasting [16]. In this paper, the membership function and fuzzy rule is obtained by historical solar power generation.…”

Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

Chang¹,

Miao²

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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Abstract-This paper proposes an adaptive network-based fuzzy inference system (ANFIS) based forecasting method for short-term solar power forecasting. An accurate forecasting method for power generation of the photovoltaic (PV) system is urgent needed under the relevant issues associated with the high penetration of solar power in the electricity system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of solar power generation of a PV system installed on the St. John's University of Taiwan. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate.

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Section: Adaptive Network-based Fuzzy Inference Systemmentioning

confidence: 99%

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

Chang¹,

Miao²

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

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“…Authors in [4] discuss ARIMA modeling of traffic in an institutional wireless network. A good discussion on the application of ANFIS to forecast Internet traffic time series can be found in [14]. ANFIS method is compared with ARIMA in [15] for forecasting WiMAX traffic time series.…”

Section: Related Workmentioning

confidence: 99%

“…Adaptive neuro fuzzy inference system (ANFIS) model [13] has been applied to forecast Internet traffic time series in [14]. Although other soft computing approaches have been tried earlier, ANFIS was not attempted prior to [14] in our knowledge. ANFIS is a combination of fuzzy logic and neural network approaches and inherently carries the advantages of both.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

Yadav

Balakrishnan

2014

J Wireless Com Network

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Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) for modeling of wireless network traffic in terms of typical statistical indicator and computational complexity has been attempted. ARIMA has been widely used in this area for past many years. On the other hand, ANFIS is comparatively new, and no network traffic modeling using ANFIS was attempted until recently to the best of our knowledge. At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature. Reportedly, ANFIS provides a good precision in prediction in terms of statistical indicators and also gives effective description of network conditions at different times. However, the computational complexity of ANFIS for traffic modeling is a major concern and deserves a closer inspection. In our case of wireless network traffic, as a final result, we find that ANFIS model performs better than the best ARIMA model in three different scenarios.

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“…ANFIS can be used to predict time series, such as the stock market, maritime weather, and others. The application of the ANFIS method in several research concerning the stock market forecasting of the stock exchange in Istanbul resulted in an accuracy of 98.3% [9], Maritime weather prediction in Java produces the smallest average error value of 0.00122 [10], closing price index predictions on the stock exchange [11], time series predictions from internet traffic [12], and the others. Based on several previous research, this research will build a decision support system using the Adaptive Neuro Fuzzy Inference System method to predict the benefits of the port based on the results of forecasting data throughput one year in the future using the Time Series-Adaptive Neuro Fuzzy Inference System.…”

Section: Introductionmentioning

confidence: 99%

Company Profit Prediction Based On Forecasting Of Port Throughput Using Time Series-Adaptive Neuro Fuzzy Inference System

Swindiarto

Irawan

2022

IJPS

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As a maritime country, ports play an important role in economic development in Indonesia. Throughput is an important factor affecting Port Profits. This prediction is needed in an effort to find out the company's prospects, help estimate the long-term profitability of representatives, predict earnings, and estimate risk in investment. In this research, forecasting data throughput will be carried out, such as container traffic, number of ships, export traffics, goods traffic, animal flow and passenger traffic for the next year using Time Series-Adaptive Neuro Fuzzy Inference System (TS-ANFIS) as an input parameter in the decision support system. Before predicting the benefits of the port using the ANFIS method, principal component analysis (PCA) was applied to reduce parameters that did not sufficiently affect the profits of the port. The data used are time series data from 2009 to 2018. From the system built it is expected to be able to provide good results in predicting the value of port throughput using TS-ANFIS and to predict profit values using the ANFIS method. The best results from profit prediction using ANFIS obtained R2 of 0.947, RMSE of 28524582.39, MAPE of 14.74% and MAAPE of 0.145. From the prediction results, it can be used as a reference for company projections in investing, managing cash flow, managing assets and global bonds.

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ANFIS method for forecasting internet traffic time series

Cited by 20 publications

References 11 publications

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

Short-Term Solar Power Forecasting Using the Adaptive Network-Based Fuzzy Inference System

Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

Company Profit Prediction Based On Forecasting Of Port Throughput Using Time Series-Adaptive Neuro Fuzzy Inference System

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