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The quality of multimedia communicated through the Internet is highly sensitive to packet loss. In this letter, we develop a time-series prediction model for the end-to-end packet loss rate (PLR). The estimate of the PLR is needed in several transmission control mechanisms such as the TCP-friendly congestion control mechanism for UDP traffic. In addition, it is needed to estimate the amount of redundancy for the forward error correction (FEC) mechanism. An accurate prediction would therefore be very valuable. We used a relatively novel prediction model called sparse basis prediction model. It is an adaptive nonlinear prediction approach, whereby a very large dictionary of possible inputs are extracted from the time series (for example, through moving averages, some nonlinear transformations, etc.). Only few of the very best inputs among the dictionary are selected and are combined linearly. An algorithm adaptively updates the input selection (as well as updates the weights) each time a new time sample arrives in a computationally efficient way. Simulation experiments indicate significantly better prediction performance for the sparse basis approach, as compared to other traditional nonlinear approaches. Index Terms--Adaptive nonlinear prediction, packet loss, packet loss prediction, sparse basis.

The purpose of this research was to construct a Markov model of digital therapeutics to predict the lifetime costs and consequences that would be incurred by a hypothetical group of adult smokers in Korea who only made a single attempt to stop smoking. To determine the efficacy of DTx, we created an annual cycle Markov model. The result shows that the NRT strategy is determined as the dominant strategy. Digital therapeutics acts as a complement to pharmacotherapy and is a low-cost option.