Learning short multivariate time series models through evolutionary and sparse matrix computation

Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on Natural Computation and sparse matrices that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the associated parameters. Extensive results are presented, where the proposed method is compared with both traditional statistical and heuristic search techniques and evaluated on a number of criteria. The results have implications for a wide range of applications involving the learning of short MTS models

Knowledge extraction from the behaviour of players in a web browser game

Dissertação de mestrado em Informatics EngineeringThe analysis of the player’s behaviour is a requirement with growing popularity in the traditional computer games segment and has been proven to aid the developers create better and more profitable games. There is now interest in trying to replicate this attainment in a less conventional genre of games known as web browser games. The main objective of this work is to analyse and create a technique for the analysis of the behaviour of the players inside a web browser game. For this analysis a system to automatically collect, process and store the relevant data for the referred analysis was developed. The web browser game used as a case study for this work is developed by 5DLab and is called Wack-a- Doo. The work developed focused on creating short-term prediction models using the information collected during the first days of playing for each player. The objectives of these models are to predict the time played or the conversion state of the players. With the study of the created models it was possible to extract results that provide potentially useful information to increase the profitability of Wack-a-Doo.A análise do comportamento de jogadores é uma prática com crescente popularidade no segmento dos jogos de vídeo tradicionais. Esta técnica foi já aprovada como capaz de ajudar os criadores a desenvolver melhores e mais lucrativos jogos. Existe agora interesse em tentar replicar este sucesso num género de jogos de vídeo menos convencionais normalmente referidos como jogos de browser web. O objetivo deste trabalho é analisar e criar uma técnica para essa análise do comportamento dos jogadores de um jogo de browser web. Para isto um sistema automático de recolha, processamento e armazenamento dos dados relacionados com o comportamento dos jogadores foi desenvolvido. O jogo de browser web usado para este estudo foi criado pela empresa 5DLab e dá pelo nome de Wack-a-Doo. O trabalho desenvolvido centrou-se em fazer modelos de previsão de curto prazo usando as informações recolhidas durante os primeiros dias de jogo de cada jogador. Estes modelos têm como objetivo prever o tempo jogado e o estado de conversão do jogador. Estudando os modelos criados foi possível extrair resultados que fornecem informação potencialmente útil para melhorar a rentabilidade do Wack-a-Doo