Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

Adaptive inferential sensors based on evolving fuzzy models

A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can a- ddress the challenges of the modern advanced process industry

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

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