Given the complexity and multifaceted nature of the financial markets, effective aggregation of financial time series data underscores the optimization of predictive modeling in the finance industry. This research presents an innovative approach to clustering using auto encoders designed to distill informative representations out of S&P 500 financial time series data. Our particular methodology is horizontal (stock averages) and vertical (1-hour intraday frequency) dual-dimensional, which enables us to capture temporal patterns along with contextual richness. Comparative research demonstrates auto encoder-driven clustering enhances data quality and granularity, providing actionable understanding of market behavior. These implications applied to predictive modeling would also be considered under risk management, incipient investment strategies, or just pure advanced financial analyses.
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