Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.Comment: International Journal of Modelling, Identification and Control (2014). arXiv admin note: substantial text overlap with arXiv:1308.194

Avaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológico

Objective. To evaluate predictive power of different time-series models of malaria cases in the state of Amapá, Brazil, in the period 1997-2016. Methods. This is an ecological study of time series with malaria cases registered in the state of Amapá. Ten 3 deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results. The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. The prediction of future cases of malaria in the horizons of 6 and 12 months in advance was possible. Conclusion. It is recommended the use of the ARIMA model to predict future scenarios and to anticipate planning in state health services in the Amazon Region.Objetivo. Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos. Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados. O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão. Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica

Análisis temporal y pronóstico del uso de las TIC, a partir del instrumento de evaluación docente de una Institución de Educación Superior

Los instrumentos de evaluación docente que se han aplicado desde septiembre del 2015 en la Universidad Técnica del Norte, han incorporado preguntas referentes al uso de las TIC en la práctica docente. Sin embargo, las preguntas relacionadas con esta actividad no fueron categorizadas con un subcriterio en la estructura de evaluación original, por lo que se diseñó una nueva estructura factorial que permitió extraer los puntajes del uso de las TIC para cada periodo académico, cuya validez y fiabilidad fue demostrada mediante Análisis Factorial Confirmatorio. Los valores promedio de este nuevo componente, se organizaron en una serie temporal, mediante la que se realizó un pronóstico por redes neuronales NNF de los puntajes que se tendrá en los próximos periodos académicos empleando perceptrones multicapa MLP. Los resultados reflejan una tendencia creciente en el uso e incorporación de las TIC en la práctica docente y ratifican esta tendencia para los próximos periodos

An Efficient Feature Selection Method for Activity Classification

Abstract-Feature selection is a key step for activity classification applications. Feature selection selects the most relevant features and considers how to use each of the selected features in the most suitable format. This paper proposes an efficient feature selection method that organizes multiple subsets of features in a multilayer, rather than utilizing all selected features together as one large feature set. The proposed method was evaluated by 13 subjects (aged from 23 to 50) in a lab environment. The experimental results illustrate that the large number of features (3 vs. 7 features) are not associated with high classification accuracy using a single Support Vector Machine (SVM) model (61.3% vs. 44.7%). However, the accuracy was improved significantly (83.1% vs. 44.7%), when the selected 7 features were organized as 3 subsets and used to classify 10 postures (9 motionless with 1 motion) in 3 layers via a hierarchical algorithm, which combined a rule-based algorithm with 3 independent SVM models

Forecasting tourist arrivals at attractions: Search engine empowered methodologies

© The Author(s) 2018. Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning

Feature selection for time series prediction - A combined filter and wrapper approach for neural networks

Modelling artificial neural networks for accurate time series prediction poses multiple challenges, in particular specifying the network architecture in accordance with the underlying structure of the time series. The data generating processes may exhibit a variety of stochastic or deterministic time series patterns of single or multiple seasonality, trends and cycles, overlaid with pulses, level shifts and structural breaks, all depending on the discrete time frequency in which it is observed. For heterogeneous datasets of time series, such as the 2008 ESTSP competition, a universal methodology is required for automatic network specification across varying data patterns and time frequencies. We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation. The methodology identifies time series patterns, creates and transforms explanatory variables and specifies multilayer perceptrons for heterogeneous sets of time series without expert intervention. Examples of the valid and reliable performance in comparison to established benchmark methods are shown for a set of synthetic time series and for the ESTSP’08 competition dataset, where the proposed methodology obtained second place

Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations

Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR