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- W597126532 abstract "The aim of this thesis is to analyze large datasets with mathematical modeling and frequency analysis. The models used were polynomials and trigonometric polynomials. These were applied on a sample dataset with 377 data points. Another dataset with approximately 2.6 million data points was also tested with a model of first degree polynomial and trigonometric polynomials with different number of dominant frequencies included. The results noted were the execution times of each model and the norm of residuals. These showed that the most accurate model was also the one with longest execution time. The conclusions were that trigonometric polynomials with only dominant frequencies included were more accurate than polynomials. These could also be considered to be suitable as a mathematical model for the given dataset, since these resulted in smaller residuals. Applications such as outlier determination and forecasting can be used with the methods tested in this thesis. (Less)" @default.
- W597126532 created "2016-06-24" @default.
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- W597126532 date "2014-01-01" @default.
- W597126532 modified "2023-09-26" @default.
- W597126532 title "Analyzing Large Datasets with Mathematical Modeling and Frequency Analysis" @default.
- W597126532 hasPublicationYear "2014" @default.
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