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Similarity measure is a central problem in time series data mining. Although most approaches to this problem have been developed, with the rapid growth of the amount of data, we believe there is a challenging demand for supporting similarity measure in a fast and accurate way. In this paper, we propose a new time series representation model and a corresponding similarity measure, which is able to...
In general, dynamic systems are systems with time-dependent behavior. Dynamic systems are characterized by the non-stationary data sequences they emit. One particular way to model these non-stationary sequences is to consider them as a sequence of stationary segments, regimes, where each regime is separated by regime switching points from both the preceding and subsequent regimes. In system identification...
Missing values becomes one of the problems that frequently occur in the data observation or data recording process. The needs of data completeness of the observation data for the uses of advanced analysis becomes important to be solved. Conventional method such as mean and mode imputation, deletion, and other methods are not good enough to handle missing values as those method can caused bias to the...
Determining similar temporal patterns and unearthing eccentric patterns require an efficient similarity measure and approach for association patterns support estimation. This research addresses the similarity measure for revealing similar temporal patterns using a similarity measure and approach for estimating support bounds of temporal patterns. A case study is demonstrated to show working of the...
This paper presents a symbolic dynamic method for real-time estimation of battery state-of-charge (SOC). In the proposed method, symbol strings are generated by partitioning (finite-length) time windows of synchronized input-output (e.g., current-voltage) pairs in the respective two-dimensional space. Then, a special class of probabilistic finite state automata (PFSA), called D-Markov machine, is...
Symbolic time series analysis (STSA) is built upon the concept of symbolic dynamics that deals with discretization of dynamical systems in both space and time. The notion of STSA has led to the development of a pattern recognition tool in the paradigm of dynamic data-driven application systems (DDDAS), where a time series of sensor signals is partitioned to obtain a symbol sequence that, in turn,...
Power estimation and power ramp estimation is of crucial importance in renewable energy applications especially for wind power plants that is going to be the focus of this study. Intermittent supply of wind power generation can cause power ramps which are sudden change of power production in time. This is an important problem in power system that aims to keep the load and generation balance. Unbalance...
Smart Home environments [1] have become an important research topic in recent years. This paper deals with human presence detection using an ubiquitous sensor. Various solutions and supervision systems require to extract information regarding people present in the monitored environment. So both academic and industrial labs are interesting by this subject. More so than most other object-detection and...
Data preprocessing plays an important and critical role in the data mining process. Data preprocessing is required in order to improve the efficiency of an algorithm. This paper focuses on missing value estimation and prediction of time series data based on the historical values. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms...
This paper presents a novel approach for an online initial camera calibration to estimate the extrinsic parameters for vision-based intelligent driver assistance systems. The method uses the periodicity of dashed lane markings and velocity information to determine the extrinsic camera parameters: height, pitch and roll angle. A lane marking detector is utilized to convert the images of road scenes...
Many cellular processes exhibit cyclic behaviors. Hence, one important task in gene expression data analysis is to detect subset of genes that exhibit periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, unevenly sampled, and are highly contaminated with noise. This makes detection of...
The purpose of this paper is to examine the empirical relationship between tourism foreign exchange income and economic growth based on Chinese province-level data. We build a sample which comprises annual observations in 30 Chinese provinces over the period 1995 to 2007. With this sample the number of time series observation is relatively large and of the same order of magnitude as the number of...
We consider the problem of estimating the structural breaks in a long memory FARIMA process. The number m of break points as well as their locations, the order (p, d, q) and the parameters of each regime are assumed to be unknown. To estimate the unknown parameters, we propose two criteria based on the minimum description length (MDL) principle of Rissanen, namely a direct extension of MDL and an...
To estimate intracranial pressure (ICP) noninvasively, a data mining framework was proposed in our previous work. In the procedure, the mapping function plays an important role to estimate ICP based on the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV), which is translated to the estimated errors by the mapping function for each entry in the database. In this paper,...
At present, the methods of enterprise financial risk warning emphasize static function dependency or dynamic propagation of time series, which results in a unconsistent combination of the static and dynamic information. In this paper, a dynamic hierarchical naive Bayesian network model is developed for enterprise financial risk warning. The process of using the model and the methods of analyzing contribution...
We present here a universal estimation scheme for the problem of estimating the residual waiting time until the next occurrence of a zero after observing the first n outputs of a stationary and ergodic binary process. The scheme will involve estimating only at carefully selected stopping times but will be almost surely consistent. In case the process happens to be a genuine renewal process then our...
At present, the methods of risk warning emphasize static function dependency or dynamic propagation of time series, which results in a unconsistent combination of the static and dynamic information. Accordingly, this paper puts forward a dynamic hierarchical naive Bayesian network classifier for warning inflation risk. And an example is presented to explain the process of inflation risk warning and...
Two methods of the first Lyapunov exponent estimation for nonchaotic systems using time series are developed. Method's adequacy was tested on the typical systems. Analysis of the dependence between the estimation precision and the algorithm's parameters was performed.
A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least...
We consider an alternative approach based on copulas to investigate dependence structures of stationary Markov type time series vector. Based on the properties of parametric estimators of the 2SPMLE, we propose a method of parametric estimation of three-stage pseudo maximum likelihood estimation and investigate the asymptotic normality of parametric estimators.
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