A test to compare interval time series

Mining Interval Time Series

Data mining can be used to extensively automate the data analysis process. Techniques for mining interval time series, however, have not been considered. Such time series are common in many applications. In this paper, we investigate mining techniques for such time series. Specifically, we propose a technique to discover temporal containment relationships. An item A is said to contain an item B...

Boosting Interval-Based Time Series Classifiers

A supervised classification method for time series, even multivariable, is proposed. It is based on boosting very simple classifiers. They are formed only by one literal. The used predicates, such as “always” and “sometime” operate over temporal intervals and regions in the dominion of the values of the variable. These regions are obtained previously, using discretization techniques. The experi...

A Bootstrap Test for Time Series Linearity

A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time. Subba Rao and Gabr (1980) and Hinich (1982) have formulated some well-known nonparametric tests for Gaussianity and linearity based on the asymptotic distribution of the normalized bispectrum. The pro...

A Significance Test for Time Series Analysis

How to Test and Compare CASE tools

This paper describes how a practical study of computerized tools for systems development could be prepared and made. The paper suggests the use of a detailed checklist combined with a description how the practical evaluation of CASE tools should be executed. The purpose is to ensure that the evaluations of different CASE tools will be made in similar ways and that results can easily be compared...