Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Relationship between problem-based learning experience and self-directed learning readiness

Tun Hussein Onn University of Malaysia (UTHM) has been implementing Problem-Based Learning (PBL) to some degree in various subjects. However, to this day no empirical data has been gathered on the effectiveness of PBL as a methodology to develop self-directed learning (SDL) skills. The purpose of this study is to investigate self-directed learning readiness (SDLR) among UTHM students exposed to vaiying PBL exposure intensity. SDLR was measured using the modified version of Self-Directed Learning Readiness (SDLRS). Participants in this study were first-year undergraduate students at UTHM. The instrument was administrated to students in Electrical and Electronics Engineering, Civil and Environmental Engineering, and Technical Education (N=260). Data were analyzed using descriptive and inferential statistical techniques with analysis of variance (ANOVA) and the independent /'-test for equal variance for hypotheses testing. The results of this study indicate that overall SDLR level increase with PBL exposure up to exposure intensity twice, beyond which no increase in SDLR was observed with increase in PBL exposure. Within the same academic programme, results did not show a statistically significant difference of SDLR level between groups exposed to varying PBL exposure intensity. However, significant difference was found in some dimensions of the SDLR for the Technical Education students. Within the same education background, results did not show a statistically significant difference of SDLR level between groups exposed to varying PBL intensity. However, significant difference was found in some dimensions of the SDLR for students with both Matriculations and STPM background. A statistically significant difference of SDLR level was found between Electrical Engineering and Technical Education students for exposure once and in some SDLR dimensions. No statistically significant difference was found between students from different academic programme for exposure twice or thrice. The data supports the conclusion that SDLR level increases with increase in PBL exposure intensity up to a certain extent only, beyond which no increase of SDLR can be observed. The data also suggest that only certain dimensions of the SDLR improve with increased exposure to PBL

Measurements, Models, Systems and Design

531 s.

The determinants of vulnerability to currency crises: country-specific factors versus regional factors

We investigate the determinants of exchange market pressures (EMP) for some new EU member states at both the national and regional levels, where macroeconomic and financial variables are considered as potential sources. The regional common factors are extracted from these variables by using dynamic factor analysis. The linear empirical analysis, in general, highlights the importance of country-specific factors to defend themselves against vulnerability in their external sectors. Yet, given a significant impact of the common component in credit on EMP, a contagion effect is apparent through the conduit of credit market integration across these countries under investigation

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Ergonomic risk factors associated with muscuslokeletal disorders in computer workstation

Ergonomics Risk Factors (ERFs) at computer works are commonly related to Musculoskeletal Disorders (MSDs) such as repetitive movements, doing work in awkward postures and static postures while prolonged seating at works. The main objective of this study was to investigate the ergonomic risk factors associated with MSDs among employees in computer workstation. In this study, the data were obtained by structured interview using self-reported questionnaire and direct observation. The results show that there is significant association between neck and stress score with musculoskeletal symptoms and among office workers. As a conclusion, by assessing ERFs at workplace, the effectiveness of workplace interventions can be evaluated without waiting for changes in the prevalence of MSDs

Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.ANFIS, Discrete Choice Models, Error Back-propagation, Financial Crisis, Fuzzy Logic, US Economy