Time Series Data Generation/Simulation- Home sensors example

Max Christopher Christopher


Supervised by Alia I Abdelmoty; Moderated by Juan Hernandez Vega

This project will address the problem of limited realistic data availability for testing and evaluation in the domain of home sensor data. The collection of data in a home setting is limited as it is seen as intrusive and a privacy risk. There is, however, the need to examine and analyse large data sets, particularly for data mining and ML tasks. This project will consider some data collected over a period of almost a a year from two homes equipped with different environmental sensors and will develop method that simulate this data over longer periods of time. The project will consider the patterns in the data and the relationship over time to infer reasonable patterns of normality and up normality. Using this information, the project will then expand the data over longer periods of time. Different scenarios for the data sets could be produced and their quality compared and evaluated. The project addresses a problem that is important and of interest in many domains.

The project will provide a chance to practice experience of learnt modules on time-series data analysis, ML, programming.

Initial Plan (20/02/2023) [Zip Archive]

Final Report (02/06/2023) [Zip Archive]

Publication Form