A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The results of the method are compared to those from a Time Delay Neural Network and the C4.5 decision tree algorithm

Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data

This paper discusses a method of modeling temporal pattern and event detection based on Hidden Markov Model (HMM) for a continuous time series data. We also provide methods for checking model adequacy and predicting future events. These methods are applied to a real example of sludge bulking data for detecting sludge bulking for a water plant in Chicago

Detecting Temporal Patterns using Reconstructed Phase Space and Support Vector Machine in the Dynamic Data System

In this paper we present a method for detecting dynamic temporal patterns that are characteristic and predictive of significant events in a dynamic data system. We employ the Gaussian Mixture Model (GMM) to cluster the data sequence into three categories of signals, e.g. normal, patterns and events. The data sequence is then embedded into a Reconstructed Phase Space (RPS) which is topologically equivalent to the dynamics of the original system. We apply a hybrid method using Support Vector Machines (SVM) and Maximum a Posterior (MAP) to classify temporal pattern signals based on the event function. We performed two experimental applications using chaotic time series and Sludge Volume Index (SVI) series related to the Sludge Bulking problem. The proposed hybrid GMM-SVM phase space approach effectively detects temporal patterns and achieves higher predictive accuracy compared with the original RPS framework

A New Ensemble Learning Method for Temporal Pattern Identification

AbstractIn this paper we present a method for identification of temporal patterns predictive of significant events in a dynamic data system. A new hybrid model using Reconstructed Phase Space (MRPS) and Hidden Markov Model (HMM) is applied to identify temporal patterns. This method constructs phase space embedding by using individual embedding of each variable sequences. We also employ Hidden Markov Models (HMM) to the multivariate sequence data to categorize multi-dimensional data into three states, e.g. normal, patterns and events. A support vector machine optimization method is used to search an optimal classifier to identify temporal patterns that are predictive of future events. We performed two experimental applications using chaotic time series and natural gas usage series related to the natural gas usage forecasting problem. Experiments show that the new method significantly outperforms the original RPS framework and neural network method

Prediction of Anomalies for a Dynamic Data System in an Optimized Approach

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Particle swarm based arc detection on time series in pantograph-catenary system

Pantograph-catenary system is the most important component for transmitting the electric energy to the train. If the faults have not detected in an early stage, energy can disrupt the energy and this leads to more serious faults. The arcs occurred in the contact point is the first step of a fault. When they are detected in an early stage, catastrophic faults and accidents can be avoided. In this study, a new approach has been proposed to detect arcs in pantograph-catenary system. The proposed method applies a threshold value to each video frame and the rate of sudden glares are converted to time series. The phase space of the obtained time series is constructed and the arc event is found by using particle swarm optimization. The proposed method is analyzed by using real pantograph-videos and good result have been obtained.Pantograph-catenary system is the most important component for transmitting the electric energy to the train. If the faults have not detected in an early stage, energy can disrupt the energy and this leads to more serious faults. The arcs occurred in the contact point is the first step of a fault. When they are detected in an early stage, catastrophic faults and accidents can be avoided. In this study, a new approach has been proposed to detect arcs in pantograph-catenary system. The proposed method applies a threshold value to each video frame and the rate of sudden glares are converted to time series. The phase space of the obtained time series is constructed and the arc event is found by using particle swarm optimization. The proposed method is analyzed by using real pantograph-videos and good result have been obtained

Mining temporal reservoir data using sliding window technique

Decision on reservoir water release is crucial during both intense and less intense rainfall seasons. Even though reservoir water release is guided by the procedures, decision usually made based on the past experiences. Past experiences are recorded either hourly, daily, or weekly in the reservoir operation log book. In a few years this log book will become knowledge-rich repository, but very difficult and time consuming to be referred. In addition, the temporal relationship between the data cannot be easily identified.In this study window sliding technique is applied to extract information from the reservoir operational database: a digital version of the reservoir operation log book.Several data sets were constructed based on different sliding window size. Artificial neural network was used as modelling tool.The findings indicate that eight days is the significant time lags between upstream rainfall and reservoir water level.The best artificial neural network model is 24-15-3