The Extended Edit Distance Metric

Similarity search is an important problem in information retrieval. This similarity is based on a distance. Symbolic representation of time series has attracted many researchers recently, since it reduces the dimensionality of these high dimensional data objects. We propose a new distance metric that is applied to symbolic data objects and we test it on time series data bases in a classification task. We compare it to other distances that are well known in the literature for symbolic data objects. We also prove, mathematically, that our distance is metric.Comment: Technical repor

Dynamic PRA: an Overview of New Algorithms to Generate, Analyze and Visualize Data

State of the art PRA methods, i.e. Dynamic PRA (DPRA) methodologies, largely employ system simulator codes to accurately model system dynamics. Typically, these system simulator codes (e.g., RELAP5 ) are coupled with other codes (e.g., ADAPT, RAVEN that monitor and control the simulation. The latter codes, in particular, introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, variable uncertainties) elements into the simulation. A typical DPRA analysis is performed by: 1. Sampling values of a set of parameters from the uncertainty space of interest 2. Simulating the system behavior for that specific set of parameter values 3. Analyzing the set of simulation runs 4. Visualizing the correlations between parameter values and simulation outcome Step 1 is typically performed by randomly sampling from a given distribution (i.e., Monte-Carlo) or selecting such parameter values as inputs from the user (i.e., Dynamic Event Tre

One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification

For the last few decades, optimization has been developing at a fast rate. Bio-inspired optimization algorithms are metaheuristics inspired by nature. These algorithms have been applied to solve different problems in engineering, economics, and other domains. Bio-inspired algorithms have also been applied in different branches of information technology such as networking and software engineering. Time series data mining is a field of information technology that has its share of these applications too. In previous works we showed how bio-inspired algorithms such as the genetic algorithms and differential evolution can be used to find the locations of the breakpoints used in the symbolic aggregate approximation of time series representation, and in another work we showed how we can utilize the particle swarm optimization, one of the famous bio-inspired algorithms, to set weights to the different segments in the symbolic aggregate approximation representation. In this paper we present, in two different approaches, a new meta optimization process that produces optimal locations of the breakpoints in addition to optimal weights of the segments. The experiments of time series classification task that we conducted show an interesting example of how the overfitting phenomenon, a frequently encountered problem in data mining which happens when the model overfits the training set, can interfere in the optimization process and hide the superior performance of an optimization algorithm