Search

Topic. If we need to predict the future economic development of the state it is necessary to build indicators that could be detectors of economic development. These detectors are integral indices that can describe the overall state of the economy of the state and can warn of turning points in the development in the future. The paper discusses methods of constructing such integral indices and compares them with the rates of industrial production. We provide analysis how to improve the prediction accuracy of the integrated indices through the use of methods of combining forecasts. Combining forecasts proved to be in practice an adequate method of improving the accuracy of forecasting in conditions of uncertainty of choice between individual forecasts.Purpose. The purpose of this work was the construction of three integrated indices describing the general state of the Russian economy: leading, coincident, and lagging, their statistical analysis, calculation of forecast values of the considered indices and the estimation of the influence on prediction accuracy of combining forecasts.Methodology. The study used statistical methods to construct the integrated indices as well as statistical methods of forecasting and the technique of building of combining forecasts.Results. The results of our researches have become integral indices for the Russian economy in the period from 1999 to 2016, and their statistical comparison with observed rates of industrial production. This created an opportunity for making the forecast of development of Russian economy for the next year and comparing the forecast results with the actual data for the first four months of 2017. There are built several prediction models which were combining into the overall forecast. Combining forecasts have improved the prediction accuracy.Conclusions. The result of the work allows concluding that the combining forecasts substantially improves forecasting accuracy of integrated indices and allows using the technique of amalgamated forecasts to predict “turning points” in economic development.

Recent advancements in large language models (LLMs) have opened new possibilities for developing conversational agents (CAs) in various subfields of mental healthcare. However, this progress is hindered by limited access to high-quality training data, often due to privacy concerns and high annotation costs for low-resource languages. A potential solution is to create human-AI annotation systems that utilize extensive public domain user-to-user and user-to-professional discussions on social media. These discussions, however, are extremely noisy, necessitating the adaptation of LLMs for fully automatic cleaning and pre-classification to reduce human annotation effort. To date, research on LLM-based annotation in the mental health domain is extremely scarce. In this article, we explore the potential of zero-shot classification using four LLMs to select and pre-classify texts into topics representing psychiatric disorders, in order to facilitate the future development of CAs for disorder-specific counseling. We use 64,404 Russian-language texts from online discussion threads labeled with seven most commonly discussed disorders: depression, neurosis, paranoia, anxiety disorder, bipolar disorder, obsessive-compulsive disorder, and borderline personality disorder. Our research shows that while preliminary data filtering using zero-shot technology slightly improves classification, LLM fine-tuning makes a far larger contribution to its quality. Both standard and natural language inference (NLI) modes of fine-tuning increase classification accuracy by more than three times compared to non-fine-tuned training with preliminarily filtered data. Although NLI fine-tuning achieves slightly higher accuracy (0.64) than the standard approach, it is six times slower, indicating a need for further experimentation with NLI hypothesis engineering. Additionally, we demonstrate that lemmatization does not affect classification quality and that multilingual models using texts in their original language perform slightly better than English-only models using automatically translated texts. Finally, we introduce our dataset and model as the first openly available Russian-language resource for developing conversational agents in the domain of mental health counseling., Competing Interests: The authors declare that they have no competing interests., (© 2024 Koltcov et al.)

Topic modeling is a widely used instrument for the analysis of large text collections. In the last few years, neural topic models and models with word embeddings have been proposed to increase the quality of topic solutions. However, these models were not extensively tested in terms of stability and interpretability. Moreover, the question of selecting the number of topics (a model parameter) remains a challenging task. We aim to partially fill this gap by testing four well-known and available to a wide range of users topic models such as the embedded topic model (ETM), Gaussian Softmax distribution model (GSM), Wasserstein autoencoders with Dirichlet prior (W-LDA), and Wasserstein autoencoders with Gaussian Mixture prior (WTM-GMM). We demonstrate that W-LDA, WTM-GMM, and GSM possess poor stability that complicates their application in practice. ETM model with additionally trained embeddings demonstrates high coherence and rather good stability for large datasets, but the question of the number of topics remains unsolved for this model. We also propose a new topic model based on granulated sampling with word embeddings (GLDAW), demonstrating the highest stability and good coherence compared to other considered models. Moreover, the optimal number of topics in a dataset can be determined for this model., Competing Interests: The authors declare there are no competing interests., (©2024 Koltcov et al.)

The random forest algorithm is one of the most popular and commonly used algorithms for classification and regression tasks. It combines the output of multiple decision trees to form a single result. Random forest algorithms demonstrate the highest accuracy on tabular data compared to other algorithms in various applications. However, random forests and, more precisely, decision trees, are usually built with the application of classic Shannon entropy. In this article, we consider the potential of deformed entropies, which are successfully used in the field of complex systems, to increase the prediction accuracy of random forest algorithms. We develop and introduce the information gains based on Renyi, Tsallis, and Sharma-Mittal entropies for classification and regression random forests. We test the proposed algorithm modifications on six benchmark datasets: three for classification and three for regression problems. For classification problems, the application of Renyi entropy allows us to improve the random forest prediction accuracy by 19-96% in dependence on the dataset, Tsallis entropy improves the accuracy by 20-98%, and Sharma-Mittal entropy improves accuracy by 22-111% compared to the classical algorithm. For regression problems, the application of deformed entropies improves the prediction by 2-23% in terms of R2 in dependence on the dataset., Competing Interests: The authors declare there are no competing interests., (©2024 Ignatenko et al.)