Informatica

Integration of algorithms of investment theory and artificial intelligence allows one to create a support system for investors in exchange markets based on the ensemble of long-short-term-memory (LSTM) based recurrent neural networks (RNN). The proposed support system contains five stages: preparation of historical data, prediction by an ensemble of LSTM RNNs, assessment of prediction distributions, investment portfolio formation and verification. The prediction process outputs a multi-modal distribution, which provides useful information for investors. The research compares four different strategies based on a combination of distribution forecasting models. The high-low strategy helps decision-makers in exchange markets to recognize signals of transactions and fix limits for expectations. A combination of high-low-daily-weekly predictions helps investors to make daily transactions with knowing distribution of exchange rates during the week. The shift in time of five hours between London and New York inspired us to create a UK-NY strategy, which allows investors to recognize the signals of the market in a very short time. The joined high-low-UK-NY strategy increases the possibility of recognizing the signals of transactions in a very short time and of fixing the limits for day trading. So, this support system for investors is verified as a profitable tool for speculators in the relatively risky currency market.

The presented article is about a research using artificial neural network (ANN) methods for compound (technical and fundamental) analysis and prognosis of Lithuania's National Stock Exchange (LNSE) indices LITIN, LITIN-A and LITIN-VVP. We employed initial pre-processing (analysis for entropy and correlation) for filtering out model input variables (LNSE indices, macroeconomic indicators, Stock Exchange indices of other countries such as the USA – Dow Jones and S&P, EU – Eurex, Russia – RTS). Investigations for the best approximation and forecasting capabilities were performed using different backpropagation ANN learning algorithms, configurations, iteration numbers, data form-factors, etc. A wide spectrum of different results has shown a high sensitivity to ANN parameters. ANN autoregressive, autoregressive causative and causative trend model performances were compared in the approximation and forecasting by a linear discriminant analysis.

The paper presents an intelligent GIS architecture that enables us to extend GIS functionality by using domain specific knowledge and inference engine. In this architecture, an intelligent agent monitors events, which occur in the GIS environment, and execute tasks depending on user's actions. The intelligent agent includes an expert system shell and knowledge base. A hybrid knowledge representation method is used that integrates rule-based, object-oriented, and procedural knowledge representations.

In general terms some situations are described which require the exploitation of heuristics either to solve a mathematical optimization problem or to analyse results. A possibility to implement heuristic knowledge for selecting a suitable algorithm depending on available problem data and information retrieved from the user, is investigated in detail. We describe some inference strategies and knowledge representations that can be used in this case, and the rule-based implementation within the EMP system for nonlinear programming. Case studies are presented which outline on the one hand the heuristic recommendation of an optimization code and the achieved numerical results on the other hand.

In this report an expert system AKU for diagnostics in acupuncture is described. The injured vital energy channels can be diagnosed using three independent methods: inquiring, Ryodoraku test and Akabane test. The inquiring is constructed as a set of trees whose internal vertices are questions while the leaves are the symptoms of diseases. The production rules describe the correspondence between the symptoms and the state of vital energy “qi” in the channels. AKU is realized by IBM PC computer and used for acupuncture treatment. The program is coded in Turbo Prolog.