Evaluation of short-term traffic forecasting algorithms in wireless networks

Papadopouli, Maria; Raftopoulos, Elias; Shen, Haipeng

doi:10.1109/ngi.2006.1678229

Cited by 19 publications

(13 citation statements)

References 12 publications

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“…Forecasting Algorithms: Forecasting and developing predictive models is a well studied area with applications to weather monitoring [28], modeling predicted energy harvesting [29], and traffic forecasting [30]. Forecasting algorithms include time-series based algorithms like autoregressive and moving average models, kalman filters [31], [2] and machine learning algorithms such as Bayesian predictors [32] and support vector machines [33].…”

Section: Related Workmentioning

confidence: 99%

Predictability of energy use in homes

Lachut

Banerjee

Rollins

2014

International Green Computing Conference

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Predictability of home energy usage forms the basis of many home energy management and demand-response systems. While existing studies focus on designing more accurate prediction algorithms, a comprehensive energy management solution requires a broad understanding of prediction accuracy at different granularities, for example appliance and home, as well as different time horizons, for example an hour, day, or week into the future. In this paper, we undertake an analysis of predictability of power draw of appliances and whole-home energy consumption at four different time horizons: an hour, a quarter-day, a day, and a week in the future. Our analysis presents two research contributions. Our first contribution is a diverse dataset, GreenHomes, that includes appliance power draw and whole-home energy consumption data from seven homes across three states in the United States over a twoyear period. Our second and primary contribution is a set of insights into the predictability of home energy usage. We show that simple statistic-based algorithms perform as well as sophisticated machine learning algorithms and time-series based predictors. These simple algorithms can considerably reduce the computational need for large-scale predictive analysis of home energy data. We also show that appliance-level power draw is more predictable than whole-home energy consumption at shorter time horizons while home-level energy consumption is more predictable at longer time horizons. Finally, we show that there is large variation in predictability across homes. This variation may be attributed to home type and points to the need for personalized energy management systems.

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Section: Related Workmentioning

confidence: 99%

Predictability of energy use in homes

Lachut

Banerjee

Rollins

2014

International Green Computing Conference

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“…The aggregate demand between any two adjacent points is modeled as a multiple linear regression model. Short-term (e.g., sec-onds or minutes) forecasting of Internet traffic is addressed in (Basu, 1999); (Sang, 2000); (Papadopouli et al, 2006). The authors in (Randhawa & Hardy, 1998) model the VBR sources as AutoRegressive AR(1) Modulated Deterministic Arrival process which characterizes the inter-frame as well as the intra-frame bit rate variations, and they model the call arrival process as conventional birthdeath Markov Process.…”

Section: Arima Based Traffic Forecastingmentioning

confidence: 99%

Traffic Pattern Prediction Based Spectrum Sharing for Cognitive Radios

Li¹,

Zekavat²

2009

Cognitive Radio Systems

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“…In general, the forecasting methods can be applied either offline (e.g., [2], [3]) or online (e.g., [7], [8]). In the former, one collects samples of traffic to perform data analysis later.…”

Section: Introductionmentioning

confidence: 99%

Comparison analysis of statistical control charts for quality monitoring of network traffic forecasts

Matias

Carvalho

Araújo

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Traffic engineering in computer and communication networks has become a very complex process. An important part of this activity is to make predictions about network traffic. Therefore, research advances in network traffic forecasting are continually emerging in the literature. Although its importance, the quality control of network traffic forecasts is commonly neglected. In this paper we analyze the performance of CUSUM, Shewhart, and EWMA control charts applied to the residuals of an ARMA(2,2) forecasting model processing real WAN and LAN traffic samples. Numerical results and graphical analysis indicate that CUSUM performs worst for all traffic samples. Shewhart chart performs better than CUSUM, but worse than EWMA since it reports false alarms for processes under control. Differently than CUSUM and Shewhart, EWMA (λ=0.1) control chart shows the best performance for all traffic patterns, showing no false alarms.

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Evaluation of short-term traffic forecasting algorithms in wireless networks

Cited by 19 publications

References 12 publications

Predictability of energy use in homes

Predictability of energy use in homes

Traffic Pattern Prediction Based Spectrum Sharing for Cognitive Radios

Comparison analysis of statistical control charts for quality monitoring of network traffic forecasts

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