This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) - back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems - models that fuse artificial neural network with wavelets and autoregressive integrated moving average (ARIMA).
Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.
This article presents an overview of ontology based digital image representation. An ontology is a specification of a conceptualization to create a vocabulary for exchanging information, where conceptualization mean a mapping between symbols used in the computer (i.e., the vocabulary) and objects and relations in the real world. In this paper, digital image semantic annotation by ontology and a novel ontological approach that formalizes concepts and relations with respect to image representations for data mining - the Image Representations Ontology (IROn) - are examined.