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Publikācija: Nonlinear

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Nosaukums oriģinālvalodā Nonlinear, Non-stationary and Seasonal Time Series Forecasting Using Different Methods Coupled with Data Preprocessing
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Autori Artūrs Stepčenko
Jurijs Čižovs
Ludmila Aleksejeva
Juri Tolujew
Atslēgas vārdi Artificial neural networks, Markov chains, Principal component analysis, Ridge regression, Stepwise regression
Anotācija Time series forecasting is important in several applied domains because it facilitates decision-making in this domains. Commonly, statistical methods such as regression analysis and Markov chains, or artificial intelligent methods such as artificial neural networks (ANN) are used in forecasting tasks. In this paper different time series forecasting methods were compared using the normalized difference vegetation index (NDVI) time series forecasting. NDVI is a nonlinear, non-stationary and seasonal time series used for short-term vegetation forecasting and management of various problems, such as prediction of spread of forest fire and forest disease. In order to reduce input data set dimensionality and improve predictability, stepwise regression analysis and principal component analysis (PCA) were used as data pre-processing techniques. For comparing the obtained performance for the different methods, several performance criteria commonly used in forecasting statistical evaluation were calculated.
DOI: 10.1016/j.procs.2017.01.175
Hipersaite: http://www.sciencedirect.com/science/article/pii/S187705091730176X 
Atsauce Stepčenko, A., Čižovs, J., Aleksejeva, L., Tolujew, J. Nonlinear, Non-stationary and Seasonal Time Series Forecasting Using Different Methods Coupled with Data Preprocessing. Procedia Computer Science, 2017, Vol. 104, 578.-585.lpp. ISSN 1877-0509. Pieejams: doi:10.1016/j.procs.2017.01.175
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