Real - time download prediction based on the k - nearest neighbor method

Abstract

The amount of download prediction or forecast is a statement about the way things will happen in the future, often but not always based on experience or knowledge. While there is much overlap between prediction and forecast, a prediction may be a statement that some outcome is expected, while a forecast may cover a range of possible outcomes. Although guaranteed information about the information is in many cases impossible, prediction is necessary to allow plans to be made about possible developments; Howard H. Stevenson writes that prediction in business “… is at least two things: Important and hard”. In this paper a method is proposed to predict the amount of download in real-time using the k - Nearest neighbor algorithm., the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification.