Kalman Filter Based Prediction and Forecasting of Cloud Server KPIs

DATA FORECASTING USING KALMAN FILTER ' Alessandro GIROLAMI

This paper shows the "Sequential PrQjection Algorithm" application results, originated by the Bell System [1] and [2], forecasting the yearly representative traffic" values (YRV*) of III different route types of four years data (1983-1986), one step ahead. A seasonal algorithm using monthly data (MRV*) [3], will support the former, however, requiring an exact determination of parameters to succ...

Direct, prediction- and smoothing-based Kalman and particle filter algorithms

We address the recursive computation of the filtering probability density function (pdf) pnjn in a hidden Markov chain (HMC) model. We first observe that the classical path pn 1jn 1-pnjn 1-pnjn is not the only possible one that enables to compute pnjn recursively, and we explore the direct, prediction-based (P-based) and smoothingbased (S-based) recursive loops for computing pnjn. We next propo...

Influenza Trend Prediction Using Kalman Filter and Particle Filter

Background. Seasonal influenza can cause severe health problems and significant economic burdens in various regions of the world. In addition to the substantial morbidity and mortality cases caused by influenza, the emergency department crowding is also partially attributed to the influenza patients. Forecasting the influenza trends is crucial in developing effective countermeasures to mitigate...

Extended Kalman Filter Based Fuzzy Adaptive Filter

Kalman Filter Based Congestion Controller

Facing burst traffic, TCP congestion control algorithms severely decrease window size neglecting the fact that such burst traffics are temporal. In the increase phase sending window experiences a linear rise which may lead to waste in hefty proportion of available bandwidth. If congestion control mechanisms be able to estimate future state of network traffic they can cope with different circums...