Objective: Aimed to analyze and cluster Consumer Goods sales data to discover distinct patterns and trends.

Analyzed time series data, performing interpolation and adjustments to varying lengths, removing about 10% of the data for quality assurance.

Employed Dynamic Time Warping (DTW) as the matching algorithm to effectively identify similarities in time series data.

Created a NumPy-based algorithm, systematically aggregating at least two series simultaneously. This algorithm emulated the logic of traditional hierarchical clustering in metric calculation and data aggregation.

Outcome: The algorithm proved highly successful and insightful, simultaneously enabling the effortless clustering of over 10k time series.