REVIEW OF EXISTING DATA ANALYSIS METHODS FOR DEVELOPENT SYSTEM FOR DETERMINING DEMAND FOR GOODS

Abstract

In the context of digital development, society is rapidly advancing in digital technologies, resulting in the accumulation of large amounts of data on consumer behaviour. At the same time, effective analysis of information is becoming a critical factor for business success and informed management decisions. The aim of the work is to conduct a comprehensive review and systematisation of existing data analysis methods for determining demand for goods, to identify the optimal set of methods for developing a system for analysing and forecasting demand for computer equipment. The research methodology is based on a systematic approach to the analysis of scientific sources and a comparative analysis of data processing methods. The work applies methods of theoretical generalisation to systematise approaches to data analysis, including descriptive statistics (mean, median, mode, variance, standard deviation, asymmetry and excess coefficients), correlation analysis (Pearson and Spearman coefficients), machine learning methods (k-means and hierarchical clustering, dimension reduction methods, the Apriori method, the support vector method, decision trees) and time series analysis. A structural analysis of the advantages and limitations of each method in the context of forecasting demand for goods was conducted. The scientific novelty lies in the development of a comprehensive methodological approach to analysing demand for computer equipment, which integrates time series analysis to identify trends, seasonal analysis to identify cyclical patterns, descriptive statistics to assess demand variability, and interactive visualisation for effective interpretation of results. Conclusions. It has been established that for effective analysis and forecasting of demand for computer equipment, it is necessary to comprehensively apply methods of time series analysis, descriptive statistics, dimension reduction methods, seasonal analysis, and interactive visualisation.