Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/266379
Title: Studies on Machine Learning Techniques for Time Series Forecasting
Researcher: Panigrahi, S.
Guide(s): Behera, H.S.
Keywords: Engineering and Technology,Computer Science,Computer Science Theory and Methods
University: Veer Surendra Sai University of Technology
Completed Date: 2019
Abstract: Time series forecasting (TSF) assists in making better strategic decisions under uncertain circumstances so that financial crisis can be avoided, wise investments can be made, under/over contracting of utility can be avoided, staffs can be scheduled appropriately, service providers can provide better service, mankind can get prepared for natural disasters and many more.However, the accuracy in forecasting plays a vital role and achieving such is a challenging task owing to the vagueness and nonlinearity associated with most of the real world time series. Therefore, improving the forecasting accuracy has become a keen area of interest among the forecasters from different domains of science and engineering. newline
Pagination: 150 p.
URI: http://hdl.handle.net/10603/266379
Appears in Departments:Department of Computer Science and Engineering and IT

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10. chapter-1.pdfAttached File667.77 kBAdobe PDFView/Open
11. chapter-2.pdf385.98 kBAdobe PDFView/Open
12. chapter-3.pdf425.98 kBAdobe PDFView/Open
13. chapter-4.pdf1.01 MBAdobe PDFView/Open
14. chapter-5.pdf601.96 kBAdobe PDFView/Open
15. chapter-6.pdf683.78 kBAdobe PDFView/Open
16. chapter-7.pdf691.75 kBAdobe PDFView/Open
17. chapter-8.pdf597.11 kBAdobe PDFView/Open
18. chapter-9.pdf303.85 kBAdobe PDFView/Open
19. references.pdf341.2 kBAdobe PDFView/Open
1. cover page.pdf81.26 kBAdobe PDFView/Open
20. publication details.pdf143.87 kBAdobe PDFView/Open
2. certificate.pdf72.21 kBAdobe PDFView/Open
5. abstract.pdf142.58 kBAdobe PDFView/Open
6. list of abbreviations.pdf21.24 kBAdobe PDFView/Open
7. list of figures.pdf25.21 kBAdobe PDFView/Open
8. list of tables.pdf109.38 kBAdobe PDFView/Open
9. contents.pdf110.99 kBAdobe PDFView/Open
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