Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Nonlinear, non-stationary and seasonal time series forecasting using different methods coupled with data preprocessing

: Stepchenko, A.; Chizhov, J.; Aleksejeva, L.; Tolujew, J.

Volltext (PDF; )

Procedia computer science 104 (2017), S.578-585
ISSN: 1877-0509
International Conference on Tissue Engineering (ICTE) <2016, Riga>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IFF ()

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.