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Title: Multidimensional time series classification and its application to video activity recognition
Author: Sengupta, Shreeya
ISNI:       0000 0004 5369 6312
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2015
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A collection of observations made sequentially through time is known as a time series. The order in which the observations in a time series are recorded is important and is a distinct characteristic of time series. A time series may have only one dimension (unidimensional) or may have multiple dimensions (multidimensional). The research presented in this thesis considers multidimensional time series. Financial stock market, videos, medical (EEG and ECG) and speech data are all examples of multidimensional time series data. Analysis of multidimensional time series data can reveal underlying patterns, the knowledge of which can benefit several time series applications. For example, rules derived by analysing the stock data can be helpful in predicting the behaviour of the stock market and identifying the pattern of the strokes in signatures can aid signature verification. However, time series analysis is often hindered by the presence of variability in the series. Variability refers to the difference in the time series data generated at different points of time. It arises because of the stochasticity of the process generating the time series, non-stationarity of time series, presence of noise in time series and the variable sampling rate with which a time series is sampled. This research studies the effect of non-stationarity and variability on multidimensional time series analysis, with a pat1icular focus on video activity recognition. The research, firstly, studies the effect of non-stationarity, one of the causes of variability, and variability on time series analysis in general. The efficacy of several analysis models was evaluated on various time series problems. Results show that both non-stationarity and variability degrades the performance of the models consequently affecting time series analysis. Then, the research concentrates on video data analysis where space and time variabilities abound. The variability of the video content along the space and time dimension is known as spatial and temporal variability respectively. New methods to handle / minimise the effect of spatial and temporal variabilities are proposed. The proposed methods are then assessed against existing methods which do not handle the spatial and temporal variabilities explicitly. The proposed methods perform better than the existing methods, indicating that better video activity recognition can be achieved by addressing the spatial and temporal variabilities
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available