Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/42727
Title: Forecasting in data pauperism
Researcher: Pandey, Prateek
Guide(s): Kumar, Shishir and Srivastava, Sandeep
Keywords: Clustering Processing
Data Forecasting
Data Pauperism
Fuzzy Set Theory
Upload Date: 9-Jun-2015
University: Jaypee University of Engineering and Technology, Guna
Completed Date: 18-05-2015
Abstract: Any forecasting method requires some evidences on the basis of which a future event can be estimated Sometimes these evidences are present as numerical quantities and the employable techniques are called quantitative forecasting techniques Sometimes the evidences cannot or should not be represented as numerical quantities instead they may better be represented as linguistic values In such cases quantitative forecasting techniques are not useful and a fuzzy analysis is the only solution A good quality of these fuzzy based forecasting techniques is that the constraints that are applicable over traditional quantitative forecasting techniques are relaxed in the case of fuzzy based techniques For example minimum requirement for evidences in the traditional quantitative forecasting techniques is 50 and more than 100 are preferable, whereas a fuzzy based forecasting technique is found to produce good forecasts with as low as 32 evidences In some cases these evidences may be missing or unavailable which is often the case with new or innovative products The forecasting techniques that are employable in such conditions are called qualitative forecasting techniques Such techniques make use of experts opinions and complex decision making in order to derive useful forecasts This involvement of human experts makes the process vulnerable to a number of biases and imprecision Again to deal with such imprecision to some extent a fuzzy assessment of the problem may be useful newlineThe thesis examines the literature on forecasting to find the techniques of merit when the evidences are either limited or not present at all The objective of the thesis is to find an answer to the poor forecasting accuracy obtained in case of the dearth of evidences newlineData pauperism in the context of this work refers to the two states of data under which forecasting are performed newlineAn absolute absence no prior data are available to perform any evidence based forecasting newline newline
Pagination: xviii,180p.
URI: http://hdl.handle.net/10603/42727
Appears in Departments:Deaprtment of Computer Science

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01_title.pdfAttached File2.48 MBAdobe PDFView/Open
02_certificate.pdf81.57 kBAdobe PDFView/Open
03_abstract.pdf10.57 kBAdobe PDFView/Open
04_declaration.pdf16.32 kBAdobe PDFView/Open
05_acknowledgement.pdf18.15 kBAdobe PDFView/Open
06_contents.pdf34.52 kBAdobe PDFView/Open
07_list_of_tables.pdf26.78 kBAdobe PDFView/Open
08_list_of_figures.pdf20.56 kBAdobe PDFView/Open
09_abbreviations.pdf20.77 kBAdobe PDFView/Open
10_chapter1.pdf101.88 kBAdobe PDFView/Open
11_chapter2.pdf524.09 kBAdobe PDFView/Open
12_chapter3.pdf485.12 kBAdobe PDFView/Open
13_chapter4.pdf165.79 kBAdobe PDFView/Open
14_chapter5.pdf746.8 kBAdobe PDFView/Open
15_chapter6.pdf215.2 kBAdobe PDFView/Open
16_chapter 7.pdf45.46 kBAdobe PDFView/Open
17_conclusions.pdf17.61 kBAdobe PDFView/Open
18_summary.pdf7.74 kBAdobe PDFView/Open
19_bibliography.pdf108.59 kBAdobe PDFView/Open
20_list_of_publications.pdf26.23 kBAdobe PDFView/Open


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