Normalized Difference Vegetation Index (NDVI) time series is one of the most important instruments in precision agriculture. Forecasting of this index in precision agriculture allows us to define problems related to growth rates of agricultural crops in time. This Doctoral Thesis is devoted to the analysis and forecasting of nonlinear and nonstationary NDVI index time series with the use of data pre-processing, signal processing, linear algebra and machine learning methods. The aim of Doctoral Thesis is to develop a forecasting system of normalized difference vegetation index time series based on signal decomposition and sub-signal approximation approach, specialized data processing methods and machine learning. In the Thesis, NDVI time series forecasting system (NDVI FS) is proposed that makes short-term time series value forecasting for the next period using historical observations. In the developed system framework, an innovative approach to using variational mode decomposition method in NDVI time series forecasting task is realized. There is also offered an innovative approach to trained forecasting model transfer, which allows using this model to forecast other NDVI time series obtained from neighbourhood pixels. The Doctoral Thesis has been written in Latvian. It consists of Introduction; 5 chapters; Conclusion; 36 figures; 21 tables; 4 appendices; the total number of pages is 171, including appendices. The Bibliography contains 121 titles.