Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Spatio-Temporal Modeling of Documents(Izmir Institute of Technology, 2017) Yaşar, Damla; Tekir, SelmaTemporal and geographic information is important aspects of text documents. Thus, it also occurs frequently in many types of text documents in the form of temporal and geographic expressions. Spatio-temporal expressions can be normalized so that their meaning is unambiguous and can be placed on a timeline or pinpointed on a map. A general text document can contain many spatio-temporal expressions that are unrelated to their content. In this thesis, we propose estimating the focus time and focus place of documents that are defined as the time and place that the document’s content refers to. We utilize statistical knowledge from Wikipedia English to calculate association scores that are used to estimate the focus time and place contained in the document. We implement two different association score calculation methodologies and compare their accuracy respectively. The effectiveness of our methods are evaluated on three different time-tagged datasets of documents about historical events in total time frame of 4000 years. Our methods achieve average error of less than 15 years. Our methods are also able to estimate focus place of each document correctly.Master Thesis Sales History-Based Demand Prediction by Using Generalized Linear Models(Izmir Institute of Technology, 2016) Özenboy, Başar; Tekir, SelmaImproved data collection and storage capabilities make vast amounts of data available in appropriate formats. Commercial enterprises store their sales data. It’s vital for companies to accurately predict demand by utilizing the existing sales data. Such predictive analytics is a crucial part of their decision support systems to increase the profitability of the company. In predictive data analytics, the branch of regression modeling commonly is used to predict a numerical response variable like sales amount. In recent years, generalized linear models provide a generalization to better address the specificities of the problem at hand. To begin with, they relax the assumption of normally distributed error terms. Moreover, the relationship of the set of predictor variables and the response variable could be represented by a set of link functions rather than the sole choice of the identity function. This thesis models the sales amount prediction problem through the use of generalized linear models. Unique company sales data are explored and fitted accordingly with the right distribution function of the response variable along with an appropriate link function. The experimental results are compared with the other regression models, classification algorithms, and time series models. The model selection is performed via the use of MSE and AIC metrics respectively.