Published Paper Details:

This study focuses on stream selection, a critical task in fields like data processing, machine learning, and signal processing, where continuous data streams are involved. The aim is to develop a systematic approach for stream selection using statistical analysis. The study begins by collecting a representative dataset consisting of diverse data streams from various sources. Statistical techniques are then applied to analyze the characteristics of the streams, including mean, variance, skewness, kurtosis, autocorrelation, cross-correlation, trend analysis, seasonality detection, and forecasting. Based on the results, a selection algorithm is devised, considering both individual stream properties and their relationships. The algorithm prioritizes streams based on relevance and significance for analysis tasks. The proposed approach is evaluated against existing methods, employing metrics like accuracy, precision, recall, and F1 score. The results demonstrate the effectiveness of statistical analysis in stream selection, outperforming baseline methods. The study highlights the importance of leveraging statistical techniques in real-time data processing scenarios, enabling better decision-making and accurate predictions in various domains.