The International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) has become a key conduit to exchange experience and knowledge among researchers and practitioners in the field of dat...
Temporal data, which is a sequence of data tuples measured at successive time instances, is typically very large. Hence instead of mining the entire data, we are interested in dividing the huge data i...
Symbolic approximation representation is a key problem in time series which can significantly affect the accuracy and efficiency of data mining. However, since currently used methods divide the origin...
Studying human postural control mechanisms not only includes the analysis of diverse time-series datasets including position and acceleration of perturbation and changes in a subject’s anterior-poster...
Stock portfolio optimization is both an attractive research topic and a complex problem due to the rapidly changing economy. Based on optimization techniques, many algorithms have been proposed to min...
State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal pattern...
Several improvements have been done in time series classification over the last decade. One of the best solutions is to use the Nearest Neighbour algorithm with Dynamic Time Warping(DTW), as the dista...
SAX is the representative time series representation method. SAX used the PAA technique to reduce the dimension of time series. But PAA technique has the demerit that cannot represent various movement...
Planning activities are very important in the energy sector, where the utilities are seeking information that may assist in decisions regarding expansion needs and resource management, improving the q...
Pattern recognition or matching algorithms process large datasets of information that can be either images for recognition or time series in pattern matching. To gather accurate results, these algorit...
Particle Swarm Optimization algorithm (PSO), when applied to problems with continuous variables, presents results with better quality at a lower computational cost when compared to the Genetic Algorit...
PASSOS, Henrique dos Santos. Ensemble of symbolic representation techniques for biometric recognition based on ECG signals. 2018. 103 p. Dissertation (Master of Science) – School of Arts, Sciences and...
One reason for researching new biometric modalities is to improve the capabilities of security systems against threats. Biometric modalities based on biomedical signals, in particular the electrocardi...
OBJECTIVE A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to det...
Nos travaux decrits dans cette these portent sur l’apprentissage d’une representation pour la classification automatique basee sur la decouverte de motifs a partir de series temporelles. L’information...
Neighborhood discovery is a precursor to knowledge discovery in complex and large datasets such as Temporal data, which is a sequence of data tuples measured at successive time instances. Hence instea...
Motifs in time series are approximately repeated subsequence found within a long time series data. There are some popular and effective algorithms for finding motif in time series. However, these algo...
Many researchers focus on dimensionality reduction techniques for the efficient data mining in large time series database. Meanwhile, corresponding distance measures are provided for describing the re...
Long time-series, involving thousands or even millions of time steps, are common in many application domains but remain very difficult to explore interactively. Often the analytical task in such data ...
In this article, we propose a novel approach to transforming financial time-series values into the symbolic representation based on value changes. Such approach seems to have a few advantages over exi...