This predictor is dynamic in nature rather than fixed.
This predictor was determined by dividing number of shares offered by total common stock.
This predictor was already presented in [8], where it is demonstrated that QoE highly depends on downlink-encoding bottlenecks.
This predictor is labelled as Univariate Gaussian Predictor Univariate Gaussian Predictor in Algorithm 1 and Figure 2.
This predictor is a stable dead-time-compensator coping with multiple and arbitrary delays in all the signal channels.
This predictor is physically sound (i.e. exhibits the aforementioned canonical form) and appears to be commensurate to or better than previous estimates of k.
This predictor will be referred to as "the trivial predictor" and it measures the amount of stationarity in the mobility sequence.
This predictor is designed for the double-embedding scheme in which the prediction coefficients can be adaptively calculated by minimum error power method.
This predictor performed very well, scoring an RMSE of 1.087 and an R2 of 0.632, respectively beaten by only one and two individual humans.
This predictor was validated in four independent datasets derived from different laboratories in different parts of the world.
This predictor is now referred to as "cost".
