Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  propose a network traffic predictor based on a novel multifractal network traffic model. This multifractal traffic model extends the notion of the classical fractional Brownian traffic model proposed by Norros (Queueing Systems, vol.16, p.387-396, 1994), replacing the fractional Brownian motion (fBm) process by the multifractional Brownian motion (mBm) process. The network traffic is assumed to be modeled by the extended fractional Brownian traffic model, which is characterized by its Holder exponents. The value of the Holder exponent at a given time indicates the degree of the traffic burstiness at that time. Based on the mBm covariance structure, a mean-square error discrete-time k-step predictor is implemented. Tests carried out using real wide area network traffic traces proved the proposed traffic model and predictor effectiveness.
['Gabriel Rocon Bianchi', 'Flávio Henrique Teles Vieira', 'Lee Luan Ling']
I am writing a research paper on a idea A novel network traffic predictor based on multifractal traffic characteristic
[{"from": "human", "value": "I am writing a research paper on a idea A novel network traffic predictor based on multifractal traffic characteristic"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  propose a network traffic predictor based on a novel multifractal network traffic model. This multifractal traffic model extends the notion of the classical fractional Brownian traffic model proposed by Norros (Queueing Systems, vol.16, p.387-396, 1994), replacing the fractional Brownian motion (fBm) process by the multifractional Brownian motion (mBm) process. The network traffic is assumed to be modeled by the extended fractional Brownian traffic model, which is characterized by its Holder exponents. The value of the Holder exponent at a given time indicates the degree of the traffic burstiness at that time. Based on the mBm covariance structure, a mean-square error discrete-time k-step predictor is implemented. Tests carried out using real wide area network traffic traces proved the proposed traffic model and predictor effectiveness."}]