Artificial intelligence in knowledge management for Time Series Forecasting

Knowledge Management (KM) is a keen topic for an organization, in particular to those that have to deal with knowledge acquired from different sources, either from its own experiences or from that of others, to decide on the effective use of that knowledge to fulfill the goals of the organization. As representative examples of KM, one may have the object-oriented data bases, hypermedia or concept maps. On the other hand, techniques developed in Artificial Intelligence for knowledge representation and discovery may be of great use in KM; in particular, it seems natural to explore the potential of the organization past data to deal with management decisions of the present. One way is to use Time Series Forecasting (TSF), where forecasts are based on pattern recognition of past observations ordered in time. Traditional TSF methods, such as the Holt-Winters and the Box-Jenkins ones, are based on particular characteristics of the Time Series (TS), such as trend or seasonal effects. These methods work with well behaved TS, but present some drawbacks on TS with noise or some unknown nonlinear relations among the TS data. An alternative approach is the use of Artificial Neural Networks (ANNs), which present two main advantages: ANNs can extrapolate patterns from past data, even in TS with noise, and may adapt their behavior as new data comes in. A problem with the use of this approach is the search time for the best ANN architecture, which involves a large searching space, demanding a huge computational effort. Other aspect of concern is that of TS data filtering. Not all lags of the TS have the same influence over the forecast. Feeding the ANN with a big time window will slow the ANN forecasting efficiency. To solve these pitfalls, one may use random search, hill climbing or genetic procedures. The last ones are known to work well on problems of combinatorial nature, obtaining good solutions where other methods seem to fail. This paper presents an integrated approach for TSF: a set of rules will create the training cases, based on some lags of the TS; these rules and the ANN parameters will be encoded on the genetic chromosomes; finally, each ANN will be trained, leading to competition

Nepeta coerulea Aiton subsp. sanabrensis (Losa) Ubera & Valdés: uma labiada nova para a flora de Portugal

A Nepeta coerulea subsp. sanabrensis é uma subespécie nova para Portugal cujo tipo nomenclatural [basión. N. sanabrensis Losa, Contribuición al Estúdio de la Flora y Vegetación de la Província de Zamora, Inst. A.J. Cavanilles: 117] provém da vizinha região de Puebla de Sanábria. Além da Serra de Nogueira e da localidade clássica, estão publicadas na bibliografia apenas mais duas localidades para esta espécie por F. NAVARRO et al. (Stud. Bot. 10: 17-24, 1992), ambas localizadas não muito longe da Serra de Nogueira, na província espanhola de Zamora

Ensino em gestão de recursos florestais na Escola Superior Agrária do Instituto Politécnico de Bragança

O curso de Bacharelato em Gestão de Recursos Florestais (GRF) foi criado em 1989 na Escola Superior Agrária do Instituto Politécnico de Bragança, dirigido para a formação florestal numa perspectiva de floresta multifuncional e de exploração sustentada dos recursos florestais e naturais, a partir de uma conceção integrada do território. O curso foi reestruturado em 1998 e 2006 de forma a adequar-se aos modelos de licenciatura bietápica e de Bolonha, respectivamente. Decorridos 22 anos do início deste processo fizemos uma análise da experiência formativa na área da gestão de recursos florestais na ESA/IPB. Apresentam-se neste trabalho as filosofias, objetivos, planos de estudos, estatísticas de entradas de alunos e saídas de bacharéis, licenciados e mestres, empregabilidade e outros aspetos relevantes. A avaliação efetuada é enquadrada na evolução que o ensino superior, em geral, e florestal, em particular, sofreu em Portugal bem como na evolução dos recursos e da capacidade de investigação em ciências florestais e áreas afins na ESA/IPB. Conclui-se neste trabalho que o ensino florestal na ESA/IPB tem relevância quer em termos da formação de profissionais em gestão de recursos florestais quer no desenvolvimento de competências científicas em domínios estratégicos para a região e para o país

An evolutionary artificial neural network time series forecasting system

Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may be appealing. However, the main problem with this approach is on the search for the best ANN architecture. Genetic Algorithms (GAs) are suited for problems of combinatorial nature, where other methods seem to fail. Therefore, an integration of ANNs and GAs for TSF, taking the advantages of both methods, may be appealing. ANNs will learn to forecast by back-propagation. Different ANNs architectures will give different forecasts, leading to competition. At the end of the evolutionary process the resulting ANN is expected to return the best possible forecast. It is asserted that the combined strategy exceeded conventional TSF methods on TS of high non-linear degree, particularly for long term forecasts

Evolutionary design of neural networks for classification and regression

Comunicação aprovada à ICANGA March 2005, Coimbra.The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machine Learning (ML) algorithms. In this work, a novel evolutionary technique for MLP design is presented, being also used an ensemble based approach. A set of real world classification and regression tasks was used to test this strategy, comparing it with a heuristic model selection, as well as with other ML algorithms. The results favour the evolutionary MLP ensemble method.Fundação para a Ciência e Tecnologia - Project POSI/ROBO/43904/2002; FEDER

Dispersão de sementes por herbívoros silvestres: estratégias em espécies simpatricas

A endozoocoria é um mecanismo comum de dispersão de sementes, resultante da ingestão das frutificações e passagem pelo aparelho digestivo do animal. No Nordeste Transmontano várias espécies de herbívoros, como o veado, o corço, o coelho e a lebre coexistem em algumas áreas, sendo desconhecido o papel destas espécies na disseminação de sementes e na dinâmica da vegetação da região. Este trabalho teve como objetivos principais (1) verificar quais as espécies que mais contribuem com sementes no processo de dispersão; (2) identificar períodos importantes de disseminação a partir da quantidade de sementes detectadas nas deposições fecais; (3) caracterizar a viabilidade das sementes presentes nas deposições de cervídeos e lagomorfos Foi utilizado material fecal de cervídeos recolhido anteriormente numa área no vale do rio Onor, no norte do Parque Natural de Montesinho e material fecal recente para quatro espécies de herbívoros. Os resultados obtidos indicaram o veado como uma espécie com maior importância do que o corço na disseminação de sementes num ciclo anual, mas considerando simultaneamente as quatro espécies de herbívoros, o coelho destacou-se como a espécie que mais contribuiu para a disseminação de sementes, principalmente no período outonal. Foram detetados dois períodos importantes na disseminação; Março-Abril e Setembro-Outubro

Evolving Time Series Forecasting Neural Network Models

In the last decade, bio-inspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models, taking advantage of nonlinear learning and adaptive search. In the present approach, a combination of both paradigms is proposed, where the GEA's searching engine will be used to evolve candidate ANNs topologies, enhancing forecasting models that show good generalization capabilities. A comparison was performed, contrasting bio-inspired and conventional methods, which revealed better forecasting performances, specially when more difficult series were taken into consideration; i.e., nonlinear and chaotic ones.The work of Paulo Cortez was supported by the portuguese Foundation of Science & Technology through the PRAXIS XXI/BD/13793/97 grant. The work of José Neves was supported by the PRAXIS' project PRAXIS/P/EEI/13096/98

Simultaneous evolution of neural network topologies and weights for classification and regression

Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for the optimal ANN is a challenging task: the architecture should learn the input-output mapping without overfitting the data and training algorithms tend to get trapped into local minima. Under this scenario, the use of Evolutionary Computation (EC) is a promising alternative for ANN design and training. Moreover, since EC methods keep a pool of solutions, an ensemble can be build by combining the best ANNs. This work presents a novel algorithm for the optimization of ANNs, using a direct representation, a structural mutation operator and Lamarckian evolution. Sixteen real-world classification/regression tasks were used to test this strategy with single and ensemble based versions. Competitive results were achieved when compared with a heuristic model selection and other DM algorithms.Universidade do Minho. Centro Algoritmi.Fundação para a Ciência e a Tecnologia (FCT) - POSI/EIA/59899/2004

Evolution of neural networks for classification and regression

Although Artificial Neural Networks (ANNs) are important Data Mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input-output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.Fundação para a Ciência e a Tecnologia (FCT) - projecto POSI/EIA/59899/2004

Evolutionary neural network learning algorithms for changing environments

Classical Machine Learning methods are usually developed to work in static data sets. Yet, real world data typically changes over time and there is the need to develop novel adaptive learning algorithms. In this work, a number of algorithms, combining Neural Network learning models and Evolutionary Computation optimization techniques, are compared, being held several simulations based on artificial and real world problems. The results favor the combination of evolution and lifetime learning according to the Baldwin effect framework