🏵️Time Series and Dynamic Time Warping plus AI Expansion

Dynamic Time Warping (DTW) is a powerful tool for analyzing time series data, especially when dealing with timing variations. It measures similarity by flexibly warping the time axis, aligning series with different paces or local time shifts. Unlike traditional methods, DTW allows non-linear alignment, crucial for data with temporal distortions like speech or biological signals. A cost matrix represents local dissimilarity, and the algorithm finds the optimal path, indicating series similarity. DTW provides a robust way to compare time series, revealing patterns in dynamic data.