Forecasting

Box, George E. P.; Jenkins, G. M.; Reinsel, Gregory C.

doi:10.1002/9781118619193.ch5

Cited by 1,183 publications

(1,782 citation statements)

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“…where φ 1 , …, φ p are coefficients of the AR, X t is a time-series data at time t, c is a constant value, and ε t is a white noise value at time t. To obtain the coefficients of AR, we need to transform the (1) to a set of linear equations called the Yule-Walker equations [10]. When …”

Section: A Predicting the Number Of New Flowsmentioning

confidence: 99%

A Dynamic Timeout Control Algorithm in Software Defined Networks

Kim¹,

Lee²,

Lee³

et al. 2014

IJFCC

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Abstract-While Software Defined Networking (SDN) has received a considerable amount of attention, improving the scalability of an SDN controller has always been a major concern. One of the main reasons why the controller suffers from this scalability problem is that it is fairly often overwhelmed by a large number of flow setup requests from the SDN switches to the controller. These requests cause decrease in the number of switches that the controller can deal with. Since these requests are usually generated when flows arrive at the switch and their corresponding entries do not exist in the flow table due to eviction, minimizing the number of evictions also reduces the number of requests to the controller. This paper addresses the scalability problem and proposes the algorithm improving the scalability of the SDN controller by dynamically controlling the timeout value of each flow without modifying the switches. In the proposed approach, the controller collects various traffic parameters from the switches and predicts the inter-arrival times of packets in a flow. Based on the information, it dynamically adjusts the timeout value of each flow to reserve spaces in the flow table for newly arrived flows in advance. As a result, this avoids the evictions and reduces the number of flow setup requests to the controller. The benchmarking results show that the proposed algorithm reduces the number of packets to the controller by 9.9 %.

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Section: A Predicting the Number Of New Flowsmentioning

confidence: 99%

A Dynamic Timeout Control Algorithm in Software Defined Networks

Kim¹,

Lee²,

Lee³

et al. 2014

IJFCC

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show abstract

“…Since the physicsbased approach applies the physical constraints on the deformation of the heart surface motion in different directions, a physically correct and more accurate prediction is guaranteed, especially in a case of highly accurate physical model. In contrast to the physics-based model, the ARX model [30] of order 9 fits the noisy measurement data. Thereby, the predicted position of the heart surface deviates strongly from the true realization of the heart surface motion since the physical characteristics of the system are ignored.…”

Section: Simulationmentioning

confidence: 99%

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

2010

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Purpose Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions. Methods A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heart models. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking. Results Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standard model-based methods, it copes successfully with occlusions and provides high performance when all measurements are not available. Conclusions Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment.

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“…The obtained random time series data followed a normal distribution (µ=0.151, σ=1.257), as indicated by the histogram in Figure 2b. The partial autocorrelation function (pACF [45]) con rmed that the generated data came from a rst order AR process (ARIMA(1,0,0); Figure 2d). By tting an ARIMA(1,0,0) model to the generated red noise time series the parameters of the simulated series were revealed: AR(1)=0.62; intercept=0.15; σ = 0.97; loglikelihood=-280.55; AIC=567.11 [46].…”

Section: Red Noisementioning

confidence: 99%

“…white noise (Figure 1a; for details see [45]) was generated by all three integers characterizing the ARIMA (p,d,q) model being set to zero: the number of autoregressive terms, the number of non-seasonal di erences needed for stationarity, and the number of lagged forecast errors in the prediction equation (MA), as well (ARIMA(0,0,0)). Technically, it is an AR(0) process.…”

Section: White Noisementioning

confidence: 99%

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

et al. 2016

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Time series often contain breakpoints of di erent origin, i.e. breakpoints, caused by (i) shifts in trend, (ii) other changes in trend and/or, (iii) changes in variance. In the present study, arti cially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. The time series are modi ed so that the exact "locations" of the arti cial breakpoints are prescribed, making it possible to evaluate the methods exactly. Hence, the study provides a deeper insight into the behaviour of the three di erent breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in reallife data sets, as is demonstrated with two examples taken from the elds of paleoclimate research and petrology.

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Forecasting

Cited by 1,183 publications

References 0 publications

A Dynamic Timeout Control Algorithm in Software Defined Networks

A Dynamic Timeout Control Algorithm in Software Defined Networks

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

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