TIMELINE ANALYSIS: IDENTIFYING TRENDS AND SEASONAL CHANGES

Abstract

This article highlights the theoretical and practical aspects of the time series analysis approach in analyzing innovation and investment processes. The study examined methods for determining trends and seasonal changes, and analyzed the capabilities of the STL decomposition, ARIMA, and LSTM models. The results obtained demonstrate the effectiveness of these methods in economic forecasting and substantiate their importance in investment decision-making