Efficient Enriching of Synthesized Relational Patient Data with Time Series Data

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

An Efficient Time Series Data Mining Technique

Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In our study, we emphasis on the use of data mining techniques on time series, where mining techniques and tools are used in an attemp...

Efficient Multi-Relational Data Mining

On the Detection of Trends in Time Series of Functional Data

A sequence of functions (curves) collected over time is called a functional time series. Functional time series analysis is one of the popular research areas in which statistics from such data are frequently observed. The main purpose of the functional time series is to predict and describe random mechanisms that resulted in generating the data. To do so, it is needed to decompose functional ti...

Spatiotemporal analysis of remotely sensed Landsat time series data for monitoring 32 years of urbanization

The world is witnessing a dramatic shift of settlement pattern from rural to urban population, particularly in developing countries. The rapid Addis Ababa urbanization reflects this global phenomenon and the subsequent socio-economic and environmental impacts, are causing massive public uproar and political instability. The objective of this study was to use remotely sensed Landsat data to iden...