Visual Knowledge Discovery and Machine Learning for Investment Strategy

Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach for building the criteria in the multidimensional space for finding an efficient investment strategy. Criteria generated with the CPC approach allow reducing/compressing space using simple directed graphs with beginnings and the ends located in different time points. The dedicated subspaces constructed for time series include characteristics such as Bollinger Band, difference between moving averages, changes in volume etc. Extensive simulation studies have been performed in learning/testing context. Effective relations were found for one-hour EURUSD pair for recent and historical data. Also the method has been explored for one-day EURUSD time series n 2D and 3D visualization spaces. The main positive result is finding the effective split of a normalized 3D space on 4x4x4 cubes in the visualization space that leads to a profitable investment decision (long, short position or nothing). The strategy is ready for implementation in algotrading mode

Prediction Models of Financial Markets Based on Multiregression Algorithms

The paper presents the results of simulations performed for predictive goals for the main Polish index named WIG20, using the historical quotes on several connected financial time series. The data (monthly and daily tested) used to predict WIG20 are such series as economical supply of money, level of unemployment, inflation and lagged series of the main index. In order to reach prediction goal, the author's algorithms were used. These algorithms are the hybrid of two methods -- simple rules and multiregression prediction. The results reveal some interesting features of regression models, indicating the prospect of further applications of the method, especially in Internet area. The main hypothesis is that markets have a short term memory which allows to create different strategies

Covid-19: pandemic management in different parts of India

Purpose: Managing a pandemic in individual countries is a concern not only of governments but also of WHO and the entire international community. The pandemic knows no bounds. In this context, India is a special country - with a huge population and a very large diversity of cultural, geographic, economic, poverty levels, and pandemic management methods. In this work, we try to assess the sum of the impact of these factors on the state of the epidemic by creating a ranking of Indian states from the least to the most endangered. Design/methodology/approach: As a method of creating such a ranking, we take into account two very, in our opinion, objective variables - the number of deaths and the number of vaccinations per million inhabitants of the region. In order not to make the usually controversial ascribing of weights to these factors, we relate them to the selected reference region - here to the capital city - Delhi. We apply a logical principle - the more vaccinations, the better and the more deaths - the worse. Findings: The results are rather surprising. Many small regions are safe regions, such as Andaman, Tripura or Sikkim, many large or wealthy states are at the end of this ranking, such as Delhi, Maharashtra, Uttar Pradesh, Bihar, and Tamil Nadu. What was found in the course of the work? This will refer to analysis, discussion, or results. Originality/value: The method enables an indirect assessment of the quality of pandemic management in a given region of the country. It can be used for any country or even a group of countries or a continent. According to this criterion, the best state/region is intuitively the safest for residents. A small number of deaths and a large number of vaccinations may positively indicate the state of public health and good management of the fight against the pandemic by local and/or central authorities

A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology

This study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this method is the blood count parameters of an oncological patient recorded for three years with a monthly frequency. Over 20 typical measurements of these features are considered, and eight with the highest variance are selected. The registration of the overall picture of changes, a synthesis of eight fuzzy intervals, allowed for observing a systematic improvement in health. This approach allows the doctor to take a holistic view of the patient’s health (based on blood tests), avoiding the dilemma of which parameters are less and which are more important. The Mamdani fuzzy inference system was used to assess the patient’s health status. The study presents the actual results of medical measurements, and the GitHub repository contains measurement data