Generation of FARIMA(0,&amp;#x03B1;,0) sequences by recursive filtering: Testing for self-similarity

Lakehal, Mohamed Reda; Ferdi, Youcef; Taleb‐Ahmed, Abdelmalik

doi:10.1109/icecs.2009.5410865

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…FARIMA with symmetric α-stable (SαS) innovations model based fractional processes have both the heavy tailed distribution and LRD properties, where the heavy-tailed distribution and the LRD properties are also characterized by the Hurst parameter H and the heavy tailedness parameter α [28]. The Hurst parameter H of FARIMA with SαS innovation time series can be described as H = d + 1/α [15], where d(d < 1 − 1/α) is the fractional differencing exponent.…”

Section: Effects On Farima With Stable Innovations Processesmentioning

confidence: 99%

Effects of Median Filtering on Fractional Processes

Sheng

Chen

2011

Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts a and B

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Median filtering, an effective non-linear signal enhancement technique, has been successfully used for the suppression of impulsive noise and extracting features from noisy signals. Although median filtering can effectively preserve the sharp changes in signals, some signal distortion may be introduced and some features of signals may be lost. In this research, we investigate the effects of median filtering on fractional processes which are characterized by the heavy-tailed distribution or the long-range dependence (LRD). The effects of median filtering on heavy-tailed distribution characteristic of α-stable processes, and on LRD property of long-range dependent processes are investigated, respectively. Besides, the effects of median filtering on both the heavy tailed distribution and the LRD properties of fractional autoregressive integrated moving average (FARIMA) with stable innovations time series are studied. The analysis results show that the heavy-tailed distribution and the LRD properties of fractional processes are evidently affected by median filtering.

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Section: Effects On Farima With Stable Innovations Processesmentioning

confidence: 99%

Effects of Median Filtering on Fractional Processes

Sheng

Chen

2011

Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts a and B

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“…The proposed approach was recently used to develop computationally efficient algorithm for generation of fractal signals with β f / 1 -power spectra [12][13][14]. The sequences thus generated have been shown to exhibit the long-range dependence feature [15,16]. The main advantage of this approach is that it allows recursive implementation of the convolution sum defining GARMA(0, α, υ, 0) processes and thus provides considerable savings in memory requirements and great reduction in computation time per output generated sample.…”

Section: Introductionmentioning

confidence: 98%

Fast generation of generalized autoregressive moving average processes

Ferdi

2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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This paper presents a new fast algorithm for synthesizing sequences of generalized Autoregressive Moving Average (GARMA) processes. These can be used to model time series which exhibit both short-range and long-range dependencies, as well as periodic behavior. The proposed synthesis scheme is based upon parameterizing the Gegenbauer coefficients by ARMA models using well-established signal modeling techniques such as Padé, Prony, Shanks, or SteiglitzMcbride methods. The proposed method is computationally efficient, sufficiently accurate, and very simple to implement. The generated sequences can be used in simulation studies such as network traffic.

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An Approach for Bursty and Self-similar Workload Generation

Yin

Chen

et al. 2013

Lecture Notes in Computer Science

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Generation of FARIMA(0,α,0) sequences by recursive filtering: Testing for self-similarity

Cited by 4 publications

References 10 publications

Effects of Median Filtering on Fractional Processes

Effects of Median Filtering on Fractional Processes

Fast generation of generalized autoregressive moving average processes

An Approach for Bursty and Self-similar Workload Generation

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