Uncovering Hidden Patterns: A DTW Approach to Time Series Matching in Finance

Unveiling Hidden Patterns: Time Series Matching

Have you ever wondered if there's a method to uncover hidden patterns in financial time series data? In the dynamic world of finance, identifying similarities between different assets can provide valuable insights for investors. One such technique is time series matching, which uses algorithms to find corresponding patterns in historical data.

In this blog post, we will explore time series matching with a focus on dynamic time warping (DTW), an algorithm that looks for minimum distance mappings between query and reference time series. By comparing the most recent 90 days of SPY to its historical performance over the last 10 years, we can better understand how DTW works and what it reveals about market behavior.