Application of Voformer-EC Clustering Algorithm to Stock Multivariate Time Series Data

Clustering stocks based on similar increasing and decreasing trends pose a challenging problem in stock forecasting. Despite the extensive research on stock forecasting, striking a balance between effective clustering and computational speed remains an ongoing challenge. Traditional multivariate time series clustering methods are difficult to guarantee high speed with high accuracy. This study introduces the Voformer-EC model as a novel approach to address this issue, enhancing the analysis of multivariate time series data related to stocks. The Voformer-EC model incorporates time features and volatility, leveraging the Voformer neural network to extract time-related features and perform clustering. To evaluate its effectiveness, we applied the model to Nifty 50 Index data recorded every 60 minutes from February 2nd to February 28th, 2015, and compared it with a traditional approach. The results demonstrate a significant improvement in clustering accuracy using the Voformer-EC model. Building on these promising outcomes, future research aims to explore the application of the Voformer-EC model to temperature and precipitation data for identifying drought-prone areas. This implementation will enable targeted risk mitigation strategies to be employed effectively, advancing precision in addressing climate-related challenges.