Intelligent system for time series pattern identification and prediction

Mestrado em Gestão de Sistemas de InformaçãoOs crescentes volumes de dados representam uma fonte de informação potencialmente valiosa para as empresas, mas também implicam desafios nunca antes enfrentados. Apesar da sua complexidade intrínseca, as séries temporais são um tipo de dados notavelmente relevantes para o contexto empresarial, especialmente para tarefas preditivas. Os modelos Autorregressivos Integrados de Médias Móveis (ARIMA), têm sido a abordagem mais popular para tais tarefas, porém, não estão preparados para lidar com as cada vez mais comuns séries temporais de maior dimensão ou granularidade. Assim, novas tendências de investigação envolvem a aplicação de modelos orientados a dados, como Redes Neuronais Recorrentes (RNNs), à previsão. Dada a dificuldade da previsão de séries temporais e a necessidade de ferramentas aprimoradas, o objetivo deste projeto foi a implementação dos modelos clássicos ARIMA e as arquiteturas RNN mais proeminentes, de forma automática, e o posterior uso desses modelos como base para o desenvolvimento de um sistema modular capaz de apoiar o utilizador em todo o processo de previsão. Design science research foi a abordagem metodológica adotada para alcançar os objetivos propostos e envolveu, para além da identificação dos objetivos, uma revisão aprofundada da literatura que viria a servir de suporte teórico à etapa seguinte, designadamente a execução do projeto e findou com a avaliação meticulosa do artefacto produzido. No geral todos os objetivos propostos foram alcançados, sendo os principais contributos do projeto o próprio sistema desenvolvido devido à sua utilidade prática e ainda algumas evidências empíricas que apoiam a aplicabilidade das RNNs à previsão de séries temporais.The current growing volumes of data present a source of potentially valuable information for companies, but they also pose new challenges never faced before. Despite their intrinsic complexity, time series are a notably relevant kind of data in the entrepreneurial context, especially regarding prediction tasks. The Autoregressive Integrated Moving Average (ARIMA) models have been the most popular approach for such tasks, but they do not scale well to bigger and more granular time series which are becoming increasingly common. Hence, newer research trends involve the application of data-driven models, such as Recurrent Neural Networks (RNNs), to forecasting. Therefore, given the difficulty of time series prediction and the need for improved tools, the purpose of this project was to implement the classical ARIMA models and the most prominent RNN architectures in an automated fashion and posteriorly to use such models as foundation for the development of a modular system capable of supporting the common user along the entire forecasting process. Design science research was the adopted methodology to achieve the proposed goals and it comprised the activities of goal definition, followed by a thorough literature review aimed at providing the theoretical background necessary to the subsequent step that involved the actual project execution and, finally, the careful evaluation of the produced artifact. In general, each the established goals were accomplished, and the main contributions of the project were the developed system itself due to its practical usefulness along with some empirical evidence supporting the suitability of RNNs to time series forecasting.info:eu-repo/semantics/publishedVersio

Shuffle design to improve time series forecasting accuracy

Proceeding of: IEEE Congress on Evolutionary Computation, CEC'09. May 18-21, 2009. Trondheim, Norway.In this work new improvements from a previous approach of an Automatic Design of Artificial Neural Networks applied to forecast time series is tackled. The automatic process to design Artificial Neural Networks is carried out by a Genetic Algorithm. These improvements, in order to get an accurate forecasting, are related with: to shuffle train and test patterns obtained from time series values and improving the fitness function during the global learning process (i.e. Genetic Algorithm) using a new patterns set called validation apart of the two used till the moment (i.e. train and test). The object of this study is to try to improve the final forecasting getting an accurate system. Results of the Artificial Neural Networks got by our system to forecast a set of famous time series are shown.The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02-02

Shuffle design to improve time series forecasting accuracy

Proceeding of: IEEE Congress on Evolutionary Computation, CEC'09. May 18-21, 2009. Trondheim, Norway.In this work new improvements from a previous approach of an Automatic Design of Artificial Neural Networks applied to forecast time series is tackled. The automatic process to design Artificial Neural Networks is carried out by a Genetic Algorithm. These improvements, in order to get an accurate forecasting, are related with: to shuffle train and test patterns obtained from time series values and improving the fitness function during the global learning process (i.e. Genetic Algorithm) using a new patterns set called validation apart of the two used till the moment (i.e. train and test). The object of this study is to try to improve the final forecasting getting an accurate system. Results of the Artificial Neural Networks got by our system to forecast a set of famous time series are shown.The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2007-67374-C02-02

Estimation of lower extremity joint moments in Clinical Gait Analysis by using Artificial Neural Networks

Gait analysis is typically conducted using an optoelectronic system which is known as the standard method for motion analysis. Despite advance development of instruments related to the optoelectronic approach, there are still a few limitations of the traditional gait analysis which limit the accessibility for individuals who would benefit from the investigation. A newly developed three-dimension motion capture system, known as Inertial Measurement Units (IMU) was introduced as an option for gait analysis. The IMU system is a transportable camera-free motion capture system. This also motivated the principle of out-of-the lab gait analysis. To broaden the use of the new system, this PhD project was conducted to examine whether the system should be used confidently for clinical gait analysis. The main purpose of this PhD project was to examine the feasibility of incorporating a machine learning method to estimate the kinetics of gait using the kinematics data obtained from an IMU system. Firstly, as pilot studies, an artificial neural network (ANN) was trained using gait data derived from the potential input signals which were signals of marker coordinates and joint angles obtained from an IMU system (Xsens) to predict joint moments of lower extremities. Promising findings were found as the ANN could reasonably predict the target joint moments. The results also showed the generalisation ability of the ANN to estimate the joint moment that it has not seen before, for instance, the ANN could fairly predict joint moments of the contralateral limb. The Xsens system was validated against the standard motion capture system before the main estimation study of the joint moment in gait began. The results revealed that joint angles obtained from the Xsens were comparable with the optoelectronic system in the sagittal plane and less comparable in the frontal plane according to the coefficient of multiple correlation and the linear fit methods. The results from the transverse plane were non-real numbers. The ANN was then trained using the joint angles derived from the Xsens system of three different walking speeds to predict the knee abduction moment (KAM). Gait data of 15 healthy volunteers were used to train the network. The ANN performed well, shown by small values of average normalised root mean square errors. Several methods were used to enhance the ANN performance. Due to the limited number of gait data used to train the network the randomisation of the input-target output data was performed. The results showed a remarkable improvement of the ANN performance. The best KAM estimation was found when the data of marker coordinates were used to train the ANN instead of joint angles. As few as three marker coordinates could provide sufficient information for the ANN to be trained and predict the KAM accurately. Principal component analysis was also used as input data manipulation and provided a reasonable KAM prediction. Overall, the kinematic gait data obtained from the Xsens could be used to train the ANN to predict the KAM in healthy gait. There is a possibility to combine machine learning methods with IMU data to produce a clinical gait analysis without the restriction of the traditional motion laboratory