Forecasting WiMAX BS traffic by statistical processing in the wavelet domain

Stolojescu, Cristina; Cusnir, Alina; Moga, Sorin; Isar, Alexandru

doi:10.1109/isscs.2009.5206175

Cited by 12 publications

(11 citation statements)

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“…It consists of numerical values representing the total number of packets corresponding to the downlink traffic recorded every 15 minutes. In [10], we demonstrated that the time series characterizing the traffic for each BS are nonstationary (the traffic's tendency, which can be interpreted as a mean value of a random variable, varies with time). More, these time series present hidden periodicity of one day and one week.…”

Section: Resultsmentioning

confidence: 99%

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Long-range dependence in WiMAX downlink traffic

Stolojescu

Moga

Lenca

et al. 2011

ISSCS 2011 - International Symposium on Signals, Circuits and Systems

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The goal of this paper is to analyze the downlink traffic corresponding to 66 base stations (BS) composing a WiMAX network in terms of long-range dependence. We show that WiMAX traffic exhibit LRD by estimating Hurst parameter using R/S method. We classify the 66 BSs in order to establish which ones have a good localization in the topology of the network and which have not. More, the proposed analysis can highlight the presence of traffic anomalies.

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Section: Resultsmentioning

confidence: 99%

“…A single such calculation results in one point on a graph of 10 i i log R(t , n) / S(t , n) against 10 log n . By varying i t and n is obtained the plot of R/S.…”

Section: Hurst Parametermentioning

confidence: 99%

Long-range dependence in WiMAX downlink traffic

Stolojescu

Moga

Lenca

et al. 2011

ISSCS 2011 - International Symposium on Signals, Circuits and Systems

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“…The source of non-stationarity is the traffic overall tendency (Stolojescu et al, 2009). These results show that the DAV estimator is practically not polarized, robust and efficient and recommend the use of this estimator for the estimation of daily WiMAX traffic H parameter.…”

Section: A Comparison Of Some Hurst Parameter Estimators Based On Simmentioning

confidence: 99%

“…There are other data mining methodologies which can be used for the estimation of the heaviness of traffic, as for example the traffic forecasting (Stolojescu et al, 2009). More precisely, we analysed the LRD of traffic, for each BS composing the considered WiMAX network.…”

Section: Introductionmentioning

confidence: 99%

“…More precisely, we analysed the LRD of traffic, for each BS composing the considered WiMAX network. There are other data mining methodologies which can be used for the estimation of the heaviness of traffic, as for example the traffic forecasting (Stolojescu et al, 2009). So, the identification of the BS with an incorrect position in the architecture of a WiMAX network could be carried out using other approaches as well.…”

Section: Introductionmentioning

confidence: 99%

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WiMAX traffic analysis and base stations classification in terms of LRD

et al. 2013

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The goal of this paper is to highlight the particularities of Worldwide Interoperability for Microwave Access (WiMAX) traffic from a long‐range dependence perspective. The application presented in this paper consists in the estimation of the quality of base stations (BS) positioning in the architecture of a WiMAX network. Analysing the traffic of the considered WiMAX network, with an original data mining methodology based on long‐range dependence, we determine which BS have a correct position in the topology of the network and which BS have an incorrect position. These BS must be repositioned when the next session of the network maintenance will take place.

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Stock Market Trend Prediction Using High-Order Information of Time Series

et al. 2019

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Given a financial time series such as S&P 500, or any historical data in stock markets, how can we obtain useful information from recent transaction data to predict the ups and downs at the next moment? Recent work on this issue shows initial evidence that machine learning techniques are capable of identifying (non-linear) dependency in the stock market price sequences. However, due to the high volatility and non-stationary nature of the stock market, forecasting the trend of a financial time series remains a big challenge. In this paper, we introduced a new method to simplify noisy-filled financial temporal series via sequence reconstruction by leveraging motifs (frequent patterns), and then utilize a convolutional neural network to capture spatial structure of time series. The experimental results show the efficiency of our proposed method in feature learning and outperformance with 4%-7% accuracy improvement compared with the traditional signal process methods and frequency trading patterns modeling approach with deep learning in stock trend prediction. INDEX TERMS Trend prediction, convolutional neural network, financial time series, motif extraction.

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Forecasting WiMAX BS traffic by statistical processing in the wavelet domain

Abstract: International audienceThe goal of this paper is to adapt a already known traffic forecasting methodology, based on statistical data processing in the field of wavelets, to the case of WiMax networks, to predict where and when upgrading must take place

Cited by 12 publications

References 1 publication

Long-range dependence in WiMAX downlink traffic

Long-range dependence in WiMAX downlink traffic

WiMAX traffic analysis and base stations classification in terms of LRD

Stock Market Trend Prediction Using High-Order Information of Time Series

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