Abstract: Looking at data over time helps spot trends and how things connect. Because machines produce tons of time-based records, quick ways to examine them matter a lot. Built into this work sits a tool that automatically studies such sequences through correlation math. Instead of skipping gaps, it fills holes in inputs before moving forward. Normalization adjusts scales so comparisons stay fair across different sources. Noise gets filtered out carefully to keep results clear and meaningful. Each step prepares the ground for trustworthy outcomes without extra effort later on. To check how closely two sets of data match and follow each other over time, Pearson and cross-correlation methods come into play. Instead of one fixed view, a moving window tracks how connections shift through different moments. Seeing the data unfold in charts makes it easier to spot repeating shapes or unusual gaps. Tests run on both artificial examples and actual recordings show the method catches shared movements, delays, and odd behaviors well. Because of this, people spend less time digging through signals by hand while getting more consistent outcomes.

Keywords: Time Series Analysis, Correlation Measures, Pearson Correlation, Sliding Window, Data Visualization, Python.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14403